{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The `Well` object\n",
    "\n",
    "Some preliminaries..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.10.dev42+g3af8468.d20220207'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import welly\n",
    "welly.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a well from LAS\n",
    "\n",
    "In the Quick Start guide you saw how to quickly create a Project from a well with:\n",
    "\n",
    "```python\n",
    "import welly\n",
    "project = welly.from_las('path/to/well.las')\n",
    "```\n",
    "\n",
    "A `welly.Project` is a collection of `welly.Well` objects. But if you only have a single well, you may not need a Project; a Well object on its own will do. Then you could do this:\n",
    "\n",
    "```python\n",
    "well, = welly.from_las('path/to/well.las')\n",
    "```\n",
    "\n",
    "The presence of the comma after `well` unpacks the single item into the `welly` variable. (This is a Python trick, it's not a Welly thing.)\n",
    "\n",
    "Alternatively, you can use the `Well.from_las()` method to load a well by passing a filename as a `str`. This is really just a wrapper for `lasio` but instantiates a `Header`, `Curve`s, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Only engine='normal' can read wrapped files\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table><tr><th style=\"text-align:center;\" colspan=\"2\">Kennetcook #2<br><small>Long = 63* 45'24.460  W</small></th></tr><tr><td><strong>crs</strong></td><td>CRS({})</td></tr><tr><td><strong>location</strong></td><td>Lat = 45* 12' 34.237\" N</td></tr><tr><td><strong>country</strong></td><td>CA</td></tr><tr><td><strong>province</strong></td><td>Nova Scotia</td></tr><tr><td><strong>latitude</strong></td><td></td></tr><tr><td><strong>longitude</strong></td><td></td></tr><tr><td><strong>datum</strong></td><td></td></tr><tr><td><strong>section</strong></td><td>45.20 Deg N</td></tr><tr><td><strong>range</strong></td><td>PD 176</td></tr><tr><td><strong>township</strong></td><td>63.75 Deg W</td></tr><tr><td><strong>ekb</strong></td><td>94.8</td></tr><tr><td><strong>egl</strong></td><td>90.3</td></tr><tr><td><strong>gl</strong></td><td>90.3</td></tr><tr><td><strong>tdd</strong></td><td>1935.0</td></tr><tr><td><strong>tdl</strong></td><td>1935.0</td></tr><tr><td><strong>td</strong></td><td>None</td></tr><tr><td><strong>data</strong></td><td>CALI, DPHI_DOL, DPHI_LIM, DPHI_SAN, DRHO, DT, DTS, GR, HCAL, NPHI_DOL, NPHI_LIM, NPHI_SAN, PEF, RHOB, RLA1, RLA2, RLA3, RLA4, RLA5, RM_HRLT, RT_HRLT, RXOZ, RXO_HRLT, SP</td></tr></table>"
      ],
      "text/plain": [
       "Well(uwi: 'Long = 63* 45'24.460  W', name: 'Kennetcook #2', 24 curves: ['CALI', 'HCAL', 'PEF', 'DT', 'DTS', 'DPHI_SAN', 'DPHI_LIM', 'DPHI_DOL', 'NPHI_SAN', 'NPHI_LIM', 'NPHI_DOL', 'RLA5', 'RLA3', 'RLA4', 'RLA1', 'RLA2', 'RXOZ', 'RXO_HRLT', 'RT_HRLT', 'RM_HRLT', 'DRHO', 'RHOB', 'GR', 'SP'])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from welly import Well\n",
    "\n",
    "well = Well.from_las('https://geocomp.s3.amazonaws.com/data/P-129.LAS')\n",
    "well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Location is not stored correctly, with latitude stored in Section and Longitude stored in Township. Also the UWI is wrong (at the top). We can fix all this by 'remapping' some fields. This is done with a dictionary that maps a well's field to its location in the LAS file. For example, we can use the Well field ('Kennetcook #2') to as the UWI in our well with a mapping like: `{'UWI': 'WELL'}`. We can remove a bad item such as the Section name, by mapping to `None`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Only engine='normal' can read wrapped files\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table><tr><th style=\"text-align:center;\" colspan=\"2\">Kennetcook #2<br><small>Kennetcook #2</small></th></tr><tr><td><strong>crs</strong></td><td>CRS({})</td></tr><tr><td><strong>country</strong></td><td>CA</td></tr><tr><td><strong>province</strong></td><td>Nova Scotia</td></tr><tr><td><strong>latitude</strong></td><td>45.20 Deg N</td></tr><tr><td><strong>longitude</strong></td><td>63.75 Deg W</td></tr><tr><td><strong>datum</strong></td><td></td></tr><tr><td><strong>range</strong></td><td>PD 176</td></tr><tr><td><strong>ekb</strong></td><td>94.8</td></tr><tr><td><strong>egl</strong></td><td>90.3</td></tr><tr><td><strong>kb</strong></td><td>94.8</td></tr><tr><td><strong>gl</strong></td><td>90.3</td></tr><tr><td><strong>tdd</strong></td><td>1935.0</td></tr><tr><td><strong>tdl</strong></td><td>1935.0</td></tr><tr><td><strong>td</strong></td><td>None</td></tr><tr><td><strong>data</strong></td><td>CALI, DPHI_DOL, DPHI_LIM, DPHI_SAN, DRHO, DT, DTS, GR, HCAL, NPHI_DOL, NPHI_LIM, NPHI_SAN, PEF, RHOB, RLA1, RLA2, RLA3, RLA4, RLA5, RM_HRLT, RT_HRLT, RXOZ, RXO_HRLT, SP</td></tr></table>"
      ],
      "text/plain": [
       "Well(uwi: 'Kennetcook #2', name: 'Kennetcook #2', 24 curves: ['CALI', 'HCAL', 'PEF', 'DT', 'DTS', 'DPHI_SAN', 'DPHI_LIM', 'DPHI_DOL', 'NPHI_SAN', 'NPHI_LIM', 'NPHI_DOL', 'RLA5', 'RLA3', 'RLA4', 'RLA1', 'RLA2', 'RXOZ', 'RXO_HRLT', 'RT_HRLT', 'RM_HRLT', 'DRHO', 'RHOB', 'GR', 'SP'])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remap = {\n",
    "    'UWI': 'WELL',\n",
    "    'KB': 'EKB',\n",
    "    'LATI': 'SECT',\n",
    "    'LONG': 'TOWN',\n",
    "    'SECT': None,\n",
    "    'TOWN': None,\n",
    "    'LOC': None\n",
    "}\n",
    "\n",
    "well = Well.from_las('data/P-129_out.LAS', remap=remap)\n",
    "well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Well header\n",
    "\n",
    "Metadata about the well is stored in its `header` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_mnemonic</th>\n",
       "      <th>mnemonic</th>\n",
       "      <th>unit</th>\n",
       "      <th>value</th>\n",
       "      <th>descr</th>\n",
       "      <th>section</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VERS</td>\n",
       "      <td>VERS</td>\n",
       "      <td></td>\n",
       "      <td>2.0</td>\n",
       "      <td></td>\n",
       "      <td>Version</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WRAP</td>\n",
       "      <td>WRAP</td>\n",
       "      <td></td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>Version</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>STRT</td>\n",
       "      <td>STRT</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0668</td>\n",
       "      <td>START DEPTH</td>\n",
       "      <td>Well</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>STOP</td>\n",
       "      <td>STOP</td>\n",
       "      <td>M</td>\n",
       "      <td>1939.1376</td>\n",
       "      <td>STOP DEPTH</td>\n",
       "      <td>Well</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>STEP</td>\n",
       "      <td>STEP</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1524</td>\n",
       "      <td>STEP</td>\n",
       "      <td>Well</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>TLI</td>\n",
       "      <td>TLI</td>\n",
       "      <td>M</td>\n",
       "      <td>280.0</td>\n",
       "      <td>Top Log Interval</td>\n",
       "      <td>Parameter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>UWID</td>\n",
       "      <td>UWID</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Unique Well Identification Number</td>\n",
       "      <td>Parameter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>WN</td>\n",
       "      <td>WN</td>\n",
       "      <td></td>\n",
       "      <td>Kennetcook #2</td>\n",
       "      <td>Well Name</td>\n",
       "      <td>Parameter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>EPD</td>\n",
       "      <td>EPD</td>\n",
       "      <td>M</td>\n",
       "      <td>90.300003</td>\n",
       "      <td>Elevation of Permanent Datum above Mean Sea Level</td>\n",
       "      <td>Parameter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td></td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    original_mnemonic mnemonic unit          value  \\\n",
       "0                VERS     VERS                 2.0   \n",
       "1                WRAP     WRAP                 YES   \n",
       "2                STRT     STRT    M         1.0668   \n",
       "3                STOP     STOP    M      1939.1376   \n",
       "4                STEP     STEP    M         0.1524   \n",
       "..                ...      ...  ...            ...   \n",
       "137               TLI      TLI    M          280.0   \n",
       "138              UWID     UWID                       \n",
       "139                WN       WN       Kennetcook #2   \n",
       "140               EPD      EPD    M      90.300003   \n",
       "141                    UNKNOWN                       \n",
       "\n",
       "                                                 descr    section  \n",
       "0                                                         Version  \n",
       "1                                                         Version  \n",
       "2                                          START DEPTH       Well  \n",
       "3                                           STOP DEPTH       Well  \n",
       "4                                                 STEP       Well  \n",
       "..                                                 ...        ...  \n",
       "137                                   Top Log Interval  Parameter  \n",
       "138                  Unique Well Identification Number  Parameter  \n",
       "139                                          Well Name  Parameter  \n",
       "140  Elevation of Permanent Datum above Mean Sea Level  Parameter  \n",
       "141                                                         Other  \n",
       "\n",
       "[142 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well.header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Important note\n",
    "\n",
    "At present, the well's `header` contains a DataFrame with the entire LAS file header.\n",
    "\n",
    "In a future release, only the well information from the **WELL** part of the file will be stored in the well's header. (The Params data goes into the `well.location` attribute, and the Curve data goes into Welly's `Curve` objects.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Well location\n",
    "\n",
    "The well's `location` contains the location info from **PARAMS**, and will also store the well's 3D positional information, if available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location({'position': None, 'crs': CRS({}), 'country': 'CA', 'province': 'Nova Scotia', 'latitude': '45.20 Deg N', 'longitude': '63.75 Deg W', 'datum': '', 'range': 'PD 176', 'ekb': 94.8, 'egl': 90.3, 'kb': 94.8, 'gl': 90.3, 'tdd': 1935.0, 'tdl': 1935.0, 'td': None, 'deviation': None})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well.location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CRS for this well is missing; we can add one if we know it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location({'position': None, 'crs': CRS({'init': 'epsg:2038', 'no_defs': True}), 'country': 'CA', 'province': 'Nova Scotia', 'latitude': '45.20 Deg N', 'longitude': '63.75 Deg W', 'datum': '', 'range': 'PD 176', 'ekb': 94.8, 'egl': 90.3, 'kb': 94.8, 'gl': 90.3, 'tdd': 1935.0, 'tdl': 1935.0, 'td': None, 'deviation': None})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well.location.crs = welly.CRS.from_epsg(2038)\n",
    "\n",
    "well.location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now there's no position log — we need to load a deviation survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "well.location.position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add deviation data to a well\n",
    "\n",
    "Let's load another well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from welly import Well\n",
    "\n",
    "well = Well.from_las('data/P-130_out.LAS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev = np.loadtxt('data/P-130_deviation_survey.csv', delimiter=',', skiprows=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns are MD, inclination, azimuth, and TVD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 18. ,   0.3,   0. ,  18. ],\n",
       "       [ 38. ,   0.5,   0. ,  38. ],\n",
       "       [ 57. ,   1.5,   0. ,  57. ],\n",
       "       [ 84. ,   1.8,   0. ,  84. ],\n",
       "       [104. ,   0.5,   0. , 104. ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`add_deviation` assumes those are the columns, and computes a position log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "p.location.add_deviation(dev[:, :3], td=2618.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns in the position log are _x_ offset, _y_ offset, and TVD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 4.71237821e-02, 1.79999178e+01],\n",
       "       [0.00000000e+00, 1.86748917e-01, 3.79994202e+01],\n",
       "       [0.00000000e+00, 5.18340431e-01, 5.69962853e+01],\n",
       "       [0.00000000e+00, 1.29577626e+00, 8.39850594e+01]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.location.position[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.45933639e-01,  3.47023772e-01, -1.65395432e-02],\n",
       "       [ 5.90396925e-01,  3.28218888e-01, -2.63643779e+00],\n",
       "       [ 5.36457735e-01,  3.11968468e-01, -5.25632568e+00],\n",
       "       ...,\n",
       "       [-3.68094384e+00,  3.97484953e+01, -2.61112780e+03],\n",
       "       [-3.68832058e+00,  3.96833189e+01, -2.61374906e+03],\n",
       "       [-3.69619567e+00,  3.96172858e+01, -2.61637033e+03]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.location.trajectory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 196.743x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.location.plot_plan()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.location.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more about the `location` attribute, see the [Well_location](Well_location.ipynb) help."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick plot\n",
    "\n",
    "`welly` produces `matplotlib` plots easily... but they aren't all that pretty. You can pass in an Axes object as `ax`, and you can embellish the plots by adding more `matplotlib` commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FRqMRgPS30el0yM3NRZs2bXDXXXf5OGriC8uXL4dOp8P+/fuxb98+vPnmmy5vu23bNlx55ZWYOnUqLl68iLNnz6J3794YNmwYzpw5Y2uXnJwMnU4HnU6Hv//+G19++SV+/fVXD7wa4gnLly9HRUUFzp8/j6effhr/+te/bJ8Xq1atsv1tb731Vtvnik6nw/z581t87LKyMuh0OmRlZWH8+PG47rrr8M033wCA7Tg6nQ7t2rWzvZdrYnn00Ueh0+lw9OhRlJeX47fffkOnTp1aHBPxLZ8kPLfffju+/fZb2/MFCxbgjjvucNpWpVJhwIAB+O2331BcXIyvv/7aW2G2OuXl5XjxxRfx2Wef4cYbb0RYWBg4jkPfvn2xcOFCKJVKu/ZqtRo333wz9u/f75uAiV9ITEzEhAkTmvQ+eOqpp3DHHXfg4YcfRlhYGKKjo/Haa69h8ODBmDt3rtNt2rdvj6FDh9qdBSKBISIiAtdccw1+/PFHLFiwAIcOHfLasRMTE/Hwww9j7ty5+L//+z/bGaaG7Nq1CzNmzEBUVBR4nkeXLl1w4403eiFa4kk+SXgGDx4MrVaLo0ePwmKx4IcffsBtt93W4DZhYWEYP348Nm/e7KUoW59t27bBYDBg6tSpLrWvrKzE4sWL6ZdPK5eTk4NVq1a5/D6oqqrC1q1bcdNNNzmsu/nmm+0uPdR18uRJbNmyBYMHD25RvMQz0tLScO7cuQbbDBw4EG3btnXpc3zUqFHYuHGjS8eeOXOm7exNfa6//noUFBTg+PHjje5v8ODBeO655/D111/j5MmTDusZYy7FRfyLzzot15zlWbt2Lbp27Yo2bdo0uk1ycjJKSkq8EF3rVFRUhNjYWMhkMtuyoUOHIjIyEmq1Gn/99RcA4N1330VkZCTCwsLw999/47vvvvNVyMSHrr32WoSFhSElJQXx8fF4+eWXbetq3iM1t169etnWlZSUQBRFJCUlOewzKSnJrn/fxYsXERkZifDwcGRkZGDQoEEYPny4Z18Y8ShffY4nJycDgEvH/vjjj3Hrrbfik08+Qbdu3dCpUyfqpxMEfJrwLFq0CN988029l7Mul5ubi+joaA9H1nrFxMSgqKjIoWNpWVkZYmJibKeCn3jiCZSVleHcuXNQq9Uu/WIiwefXX39FRUUFNm7ciGPHjtklKjXvkZrbgQMHbOtqLhNcunTJYZ+XLl1CbGys7XlycjLKysqg1WpRVlYGtVqNO++807MvjHiUrz7Hc3NzAcClY6vVajz77LPYs2cPiouLcfPNN+Omm26iH9wBzmcJT2pqKtq3b4+VK1fi+uuvb7S9TqfDunXrMGLECC9E1zoNGTIESqUSy5Ytc6l9u3bt8OGHH+Lhhx9GdXW1h6Mj/mrkyJGYOXMmnnjiCZfah4aGYsiQIfj5558d1v30008YO3as0+0iIiIwY8YMLF++vEXxEt/ZtWsXcnNzfXKWbunSpYiPj0fnzp2btF14eDieffZZVFZW4uzZsx6KjniDT+vwfPnll1i/fj1CQ0PrbWMwGLBnzx5ce+21iIqKwqxZs7wYYesSGRmJl156CXPmzMEvv/yCiooKiKKI/fv31zs6bvz48UhOTsYXX3zh5WiJP3nkkUewdu1aZGVludT+rbfewoIFC/DRRx+hoqICpaWleP7557Ft27Z6h57rdDr88MMP6N69uztDJ16g1Wrx+++/Y/r06bjtttvQs2fPJu9j1KhR9XZob0h+fj4++eQTvPzyy3jzzTfB841/7b366qvYtWsXjEYj9Ho9PvzwQ0RGRjY5WSL+xacJT8eOHdG/f3+n695++22EhYUhJiYGd9xxBzIzM7F169YGkyPSck899RTmzZuHt99+GwkJCUhISMB9992Hf/3rXxg6dKjTbZ588km8/fbbMBgMXo6W+Iu4uDjccccdeOWVV1xqP3z4cPzxxx9YsmQJkpKSkJqain379uHvv/9Genq6rd3Fixdt9VFSU1NRUlKChQsXeuplEDebMmWKrZ/X66+/jscee6zZI22zs7MxbNgwl9tHRkYiNDQUPXv2xMqVK/Hzzz9j9uzZLm3LcRxmzZqF2NhYJCcnY+3atVixYgU0Gk2zYif+gWPU3ZwQQkgTqVQqKJVKPPTQQ3j11Vc9eqycnBzcfPPN2Lp1q0eP0xx33XUXfv75Z8THx+PUqVO+Doc0gBIeQgghhAQ9mkuLEEIIIUGPEh5CCCGEBD1KeAghhBAS9GQNrfz+++8dJvUMVmVlZYiMjLQ9t1gsAABBENx+rAKtAfkVevRsE+H2fV/Ok6/D2wLltVz+XvIHLfm30+rNOF9ciU7xGqjl7v+39+e/q69ja8l7ydexuwu9jpbzx88kTwkJCal3qirqtGz1xRdf4N5777U912q1AKSiU+72wboT+GDdSZx9cyI4jnP7/uvy5OvwtkB5LZe/l/xBS/7t1h7Jxz3f7sbvDw5HDw8k6f78d/V1bC15L/k6dneh19Fy/viZ5At0SYsQQgghQY8SHkIIIYQEPUp4CCGEEBL0Guy0TNyrsMKAN1cexV8npVmlzxdXQSHjIRd4KAQechkHhcBD4DmP9+0hhBBCWhNKeLzonm93Y392me35qHc3urTdtP4p+NeNvTwTFCGEENIKUMLjRbcNTrVLeFz14+5s/Lg722H5//45FJmpUW6IjBBCCAlulPB40Y2ZbXFd3zYwmC3Qm0ToTRboTRaYLAxmUYTZwvDH4Tx8tvG0S/tbcziPEh7iUdd9tgX7LpQBAOgqKyEkkFHC42UCzyFEIUOIwvn6jIQw5JXrcbqoEowxHMgpr3df+Vo9GGPU34d4xHtrjtuSHQCIUMt9FwwhhLQQJTx+Rq0QMG9aHwDAn0fzcdeC3fW2/XX/Rfy6/yLOvTXJS9GRYFdaacQzSw5i25lilFebbMvHdY1H26gQH0ZGCCEtQwmPnzpVUNFgskOIJ6w+nIfVh/Psli2YPRAjM+J8FBEhhLgHJTx+6mKZvtE2r13bA7cNTvVCNKS1mD4gBeeLqzB/U20/sju/2um0bZhKhj8fH4n4MJW3wiOEkGajhMdHlu3PxcM/7He6rk2kGqHKxieYiwyhPhXEvTiOw9NXd8ETV2bg/kV78cfh/HrbVujNKNYZKeEhhAQESnh8ZNPxwnrX5ZZV2x53SQzDsbwK3JTZFsPTY5FTWo1reicjJZr6UxDPkQk8/n17f2w5VYQv/jqDTSfs368j0mMxqnM8uiSG+ShCQghpGkp4fGTetD64pk8yzhdXYd3RfGy2Vl++3Ozh7ZGREAaRMTDGcFWPRChljZ/9IcQd+rWLckh2AGDzySJsPlmE8V0T0C6Gkm9CiP+jhMeHRnWOBwDcOTQNaU+vcNrmqV8ONLqfr2b2x5guCW6NjRAA4OuZbe/RcRnoGB8KkTGcKtChXXQIFDKamo8Q4r8o4fETe18Yj6X7cvHq70eavG1OaXXjjQhpBqVMwLrHrsDhi1q7Pmfvrzth125wh2j8cO8QL0dHCCGuo4THT/y4Kxv/Wn3M5faJ4SpsfHIUVHK6vEXcr7TSiL6vrnWprcBzeHJCZw9HRAghLUMJj5/gm1gsOU+rx/trT+CZiV09ExBptS6/vPri5G7onRKBCLUCUSFyhKvlkAt0+YoQElgo4fED54sr8eYq18/u1DhySeuBaAipdcvAFCSEq5CZGu3rUAghpEUo4fEDb68+3qT2T07ojAndE9AhVuOhiEhrduzVq9DlhdUAgMU7s7F4ZzbuXySt+3b2QFxBVZcJIQGIEh4/cGNmW6w4eMnl9u/8cRzv/OE8STr75kSaTJQ0294Lpbj+s631rt95toQSHkJIQKKExw+kRKvdsp/BHaIp2SEesebRK5CRQEUGCSGBixIeP9ApPsw247lFZNBWm1weIQMAE3sm4rNbMz0VHmlF+rWLwrm3JmHdkXzc/W3t5LVXvv+X7T1KCCGBiBIeHyuvMqFQpwfPcbCIDCYLg1kU8faNvfD3ySKcL65EVk55g/tYeTAP13zyN76eOQAxGqWXIifBbMm+HLvncWFK7DlfCoXAQy7jwIGDySIiKUJF7zlCSECghMeH9CYLer+yxi37OpBTjv3ZZRjblSouk5Yr1hntnhdWGHDD58779tCZH0JIIKCEx4f2nC9FfJgSBRUGt+yPkh3iDhuOF2DH2ZIG26THayATeNw5JNVLURFCSMtQwuMjF4qrcOt/d7htf/dd0cFt+yKt24C0aFzVPRHH8ytgFkXIeR5hajmysstsbR4el47JvZJ9FyQhhDQRJTw+0i4mBB/f0hdf/HUGB3Mb7qPjzPV922DetD7uD4y0ehqlDPNvr+0E72yo+l8nCinhIYQEFEp4PKzaaMFtX+7AnvOlDuvGdU1AXJgSV2TEwSKKMFsY9GbR7pd0fZbsy6WEh3jFb/svOiz7aXcOftqdg98fHI4ebSJ8EBUhhDQNJTwetPF4AWZ+vave9euO5oPjpF/UnRPCIPAcNEoBV2TEQcZzOFlQgeyS2pnQv509EAPbR9OEocQrGGOYt/YEvtl6rt42pwp0lPAQQgICJTwepJA1PsEiY0CF3ozddc4ALbp7EHieQ5HOgPT4MHROpIJvxPtKKo34eP0pu2U8B2x6cjRSokN8FBUhhDQPJTwecrGsGkaz2KxtZzTSmXnzU/SFQzwvRqPE3hfG44Vlh7DigDT1iciAEW9vwMD20fjpviE+jpAQQlxHCY8H/G9PDh7/Octj+5cJNH0E8SzGGO5ftBcrD+Y5XZ8UocKpggqIDAhRCGgbRQk4IcS/UcLjAVdkxGFQ+2gcyi1HjEaJhHAlLCKDyACRMRxopHLy5aYPSMGdQ9Mg8BwMJhH5WgMi1HKEKOjPRzzDYBYdig/WtWz/RSyr05mZZlEnhPg7+sb0gLgwJX5s4HT/kr05WLjjAoxmEdUmC8qqjEiLCbXrx1PXD7uy8cOubIflVOGWeMKRi1pM/Ghzvevbx4YCADgAHAdkJIQhMzXKS9ERQkjzUMLjA9f3a4vr+7XF4YvleO33o7hUVl1vslOf16/r4aHoSGu261wJbpq/zW7ZI+PSMblXEjrGacBxdDmVEBKYKOHxEsYYskuqwXHSr2JRBG75Yju0enOz9vfc0kN4bukh2/Pv7xqE4emx7gqXtFLdksJtj7smhWPVwyN8GA0hhLgPJTxe8MG6E/hg3UmPHiNGo/Do/knroDPUJuBHL2mR9vQK2/Mlc4aiXzu6dEUICUyU8HjBhmMFbt/norsHYWgnOqND3Gt5lmNV5Rr7L5RRwkMICViU8HjBsgeGOyxbdyQfd3+7u9Ft48OUUCsEqGQCqk0WXCipAgAs2HYO/dOiXSpuSIirZg1rjwXbztlV+K7x+sqj2Hq6GDf3b4vx3RKoPw8hJKBQwuMj47ol4OuZAzDrm/qnngCAggqD0+V/HM7H7V/uaHA0GCFNJfAc1j02EuVVJpwtqsSZokrkllbjYlk1tpwuwrqj+Vh3NB9vXd8T0we283W4hBDiMkp4vMBkEfHFX2ew7mg+9l0oa/L2s4e1h8gYCisMWHHwkm35/NsyG9iKkKbLLqnCiLc3NNouQi3HodxyyAUeMoEDByA1JhQCT2d9CCH+iRIeL+j36lpUNGM0VqxGiWt6J6O40mAr8vbouAxM6Z2EDnEad4dJWrFqowX5Wj1WH3ZeWfly/1y412HZzKFpmHtNd3eHRgghbkEJj5uVV5sw7d/bcCyvosX7KtIZ8NWWs3bL3l93Au+vO0FFB0mLfLPlLOYuP4KMBA3ytQaUV5sc2vAcoJILkAu8rUhmjViNAjKeh8BzkAkcItRy3D4k1ZsvgRBCmoQSHjcrqTS6JdlxJjJEjqt7JOGGfm2gN1mgkgseOQ4JbqLIMHf5EQDAiXyd3bqRGXEY2D4aY7vGIzU6FGoFvccIIcGBEh43ax8binNvTYLRLMJoEWG2iDCLDGYLg8kiwiIymEVpWYXe7FDVtiFlVSYs3nkBi3deAABseXoM2kSqPfVSSJDieQ7zb8vEr/tyERemRGmVEb9bZ0PfdKIQm04U4p0/jgMAEsNVCFEIkAkc/nNHf6TGhPoydEIIaTZKeDxEIeNdGjJ++aWpZftz8fAP+xvd7to+yYilYoOkma7qkYireiTann8yQ6oGnlNajeySKhRUGJBTWoUzRZVYsjcXADDynY12+/jPHf0xvluCN8MmhJBmo4THT+hNFhRXGrH7XP1zarWJVOO1a3tgVOc4qoFC3I7jOKREhyAlOsRueXmVCX86KZ55rqjSW6ERQkiLUcLjZS8tO4QF287bnseEKtCzbQQ2Hi9scLtp/VPw+nU9IBOo0CDxHpNFRL/UKFvC8+i4DDw0thMl3ISQgEMJj4dtPV2E+77bU++w9OJKo9Nk543reqJrUhg6xGkQoZZ7OkxC7DDGcKGkyuEy1vSBKZTsEEICEiU8HlRYYcCM/+xwqe2iuwchIzEMcoGHXOCkgm48R18uxOvu+243/jicb7dsap9k3DooFbll1SjQGmC0iDCaRbSJVKNdTEg9eyKEEP9BCY8HrTp0qfFGVjP+23hilJkaha9mDqAzPsSjokMdO8Mv23/RVvzycjufG4v4MJWnwyKEkBahhMeDbh2Uit5tI2G0iGBMukxgFqXh6dvPlGD+ptNN2t+e86XYfLIQk3sleyhiQoA3r++FV6b2QHm1CQu3X8D7607U2/aZq7sgTqP0YnSEENI8lPB4kMBz6J0S6XRdp3hNkxMeAHhg0T68ufIYVjw0HJEhNCydeIZc4BGrUeKhsZ2QX6HHoh0XnLZbdzQf0we2o7OOhBC/RwmPj7SNCnE6PcT54kpM+OAv6E1ivdvmllVj8Jt/4tirV3syRELAcRzeuK4n3riuJ3adK3EolLnrXCl6v7wGP947GIM6xPgoSkIIaRwlPH7g6CUtrv5wc5O2WXb/cA9FQwhgERmWZ13EIz/ux4xB7fB71kVoG5gA9+c9OZTwEEL8GiU8fqCsynHixvpEqOXIeulKD0ZDWjPGGF5efgTfbD1nW+bsctaj4zKQFKFCakwI0mJDER9G/XgIIf6NEh4/0LddpMtty6tNSHt6BWJCFfj1/mEOVXEJaQm9SbRLdl6a0g3TBqQgREEfFYSQwEZle33MbBFRpDPg+r5tAEiVl11RXGnExA83Q2+yeDI80sqoFQKus74XAeDl5Udw94LdMFnq71NGCCGBgH62+UhppREGs4j7vt+DrOwy2/LiSqPL+6gwmPHH4TxM7dOm8caEuOj9aX3w4uRuGPrWelSbLNh6uhjpz63CJzP6IlKtgFohQCXnIRd4WESGmFAF4sOpDg8hxL9RwuNhLy8/jK+3nPPY/mk4MPGEqFAFVjw0HGPe22Rb9sCiffW2/2bWAIzqHO+N0AghpFko4fGwn3fnNHkbgecwqH00QhQCeI5DjEaJqBA55AKPXedKoJIL0ChliNUo8ffJIvRtF0WJD3G7DnEabHhiFEa/u7HRtjO/3oX0eA0UMh5FOgPytQYAwLZnxiApQu3hSAkhpHGU8HjYoZcn2D1njMFkYajQm/CfzWedFh+0iAxbTxe7fIzDF7VYfO/gFsdKyPniShjMIgwmEQazBXqTiC/v7C8tsz7X6c1YeegS9l0os9v2ZIHOYX+bjhdi+sB2XoqeEELqRwmPl3Ech6f/l4Ul+3Ldts9tZ4pxsawayZH0S5o0z6kCHcbN29R4w8vIeA4alQzRoQpEhyiglPM4V1SFJyd0RrfkcGQkhHkgWkIIaTpKeHzgQG652/Z1fd826J8WjaQI6jRKmu+3LOcTg9a4tk8yJvVKRqxGgeRINTRKGZQyHjKBBnoSQgIDJTw+sO6xkbbH7605jo/Xn3Jpu/en9YZaLoPAc5ALHEakx0HgOU+FSVqRR8am46bMtijSGXDoohYv/HrIbv2v+y/i18tmSw9RCHjrhl6Y0isJHEfvQ0KIf6OEx8f+OaojGAMqjWZYRAajWcTRS1pk5TieBXr0xyw8dVVn9E2JgkzgcCCnDHKBh8BzkPEceJ6DwHFg1vaMMeh0VZDxHLqFh3v3hZGAwvMcUqJDkBIdgr7tonDboHbILauG3iSiQm9Chd4Mrd6E8moT/vPXGZwrrkKV0YKHFu/DQ4v3Ycagdnjjup6+fhmEEFIvSnh8LEQhwxMTOgMAjGYRj/2032myU+Pt1cebdZwXJ3fD7OHtm7UtaX04jkPbKMcq3td+ugXniqscli/acQE8BygEAQoZD44DTubroFYIeOO6HghT0ShCQohvUcLjR3QGM34/cMnl9qkxIWAMYNZzOhykywocB9RcYGCMIUwlw6ReSe4Ol7Qiqw5ewtG8CuyvUyTzct9vd5xzC5Cqh8+9pruHIiOEENdQwuNHokMVOPvmRHy64RTeXXOiwbafzOiLyb2SG92nVqsFAIRTJVzSAo/9lIVqk8XWf0zO85AJHOSCVHFZKeMRESJHYrgKCeEqRIcqoJTxYABuHUTD0gkhvkcJj5/hOA4PjEnHA2PSna5fuOM8nlt6CAPTor0cGWnNLIzhvis64JmJXX0dCiGENAuNKSWENIoDbJ3hCSEkEFHCQwhpFM9xYIxSHkJI4KKEhxDSKI4DKN8hhAQySngIIY2iS1qEkEBHCQ8hpFEcx9EZHkJIQKOEhxDSKOkMD2U8hJDARQkPIaRx1IeHEBLgvFaHx2w2Y8WKFTh79iyqq6sRFRWFsWPHIj09HWVlZfjwww8hl9eWnx82bBhGjhxpt+2RI0cgl8sxbNgwDBkyxFuhE9Lq0dSghBAgsL/LvZbwiKKIiIgIzJw5ExERETh58iR++eUX/POf/7S1efrpp8HzjiedNm7ciJKSEjzyyCPQ6XRYsGAB4uLi0KlTJ2+FT0irxtGwdEIIAvu73GuXtBQKBUaNGoXIyEhwHIeMjAxERkbi4sWLjW6blZWFK664Amq1GnFxcejXrx/279/v+aAJIQCsw9J9HQQhxOcC+bvcZ1NL6HQ6FBcXIz4+3rbsgw8+AAB07NgR48ePR0hICKqrq6HT6ZCQkGBrl5iYiOPHmzdrOCGk6ThQHx5CiKNA+i73ScJjsViwZMkS9O7dG7GxsTAajbjnnnuQmJiIqqoqrFy5EkuWLMFtt90Go9EIAFCpaie/VCqVMBgMvgjd60wWEWYLg1kUYREZinXSv8d3288jLkwJpYxHdKgS47rGg+OopwXxDI7jaJQWIQFkz5492LNnDwCgqqrKI8cItO9yryc8jDEsXboUgiBg4sSJAKRTZMnJ0szfGo0GEydOxHvvvQeDwQCFQgEAMBgMkMlktsdKpdLboXtUSaUR32w5C5PIYBEZTBYRqw/l4VK53mn7j9efsnv+5vU9cctAmpWaeAad4SEksGRmZiIzMxMA8MUXX7h9/4H4Xe7VhIcxht9++w2VlZWYMWMGBEFotL1arYZGo0FeXh46duwIAMjPz0dcXJw3Qvaax3/ajw3HC11uf9fw9jCYLTCYRESo5bi2TxsPRkdaO44Dft6TAxnPISJEAaWMh4znIFx+4zjwPGe3Tnpc2952L0jL9dVVkPEcwqo4iIxBb7KgQ6wGESHyxgMjhHhdoH6XezXhWbFiBQoLC3HHHXfYDVvLycmBSqVCTEwMqqursXr1aqSlpdlOffXu3RubN29GcnIyKisrsXfvXkydOtWboXvcezf3wffbz0PgORjMIiyiiE0nCnEoV+u0/dU9EtE/LdrLUZLW6to+bbD2aD5+3X8RWr3JK2d72kSqwfPSxKU1iRTPSc95TkqaeA7W5VIbjoN1eW37uttKCRePtlFqJISr0D05nP4/IqSJAvW73GsJT1lZGfbs2QNBEPDuu+/alk+ePBkcx2H9+vWorKyEUqlEhw4dcMMNN9jajBo1CitWrMAHH3xgG7sfaEPSzRYReVo9OI6DxcJQXGnAdZ9tbfb+qL8O8abnJ3fD85O7AQBEkcFk7VPmcGMMZguDyBjMIpPa1nluEaU+aRaRQWcw497v9jg9XkyoAoM7xIAxaZ8ik44rMmlbkQEiq33OGKzLpcvBIgO01SacLNA1+tpemtINHeI04DmAg5QYwZpYVVVVgecATagZHCetq7nnrQlWTQImratZb9/m8m04SPc1r5X+fyaBIpC/y72W8ERGRuKll16qd33Pnj3rXSeTyTB16tSAPavDGMN1n23FwdzyJm33rxt6QikToJDxuFBShQslVXhhUjeoFQ2fPiTEk3ieg5J3z3vw3FuToNVKZzHDw8Pdss8af58swm1f7mi03cvLj7j1uE01c2ga5l7T3acxEOKqQP4u99mw9NbibFElRr+7scnbPT4+A9MGUCdkQpqrU7wG79zYC6cKdMjT6lFYYUBBhQHFOgMMZhFVRovbjqWS8whV1P9xWnMF8PLzOMmRatzUv63b4iCE1I8SHg+LUDev4+V7a0/gvbUnbM+3Pj0GyZFqd4VFSFDbn12Gaz/d0mg7zjpHmFzgEKtR2vom1QzBZyKz9gvibZedOOulq5pLWTdmtsWcUR3pshQhfo4SHg+LDlVgz/Pj8PnG09CoZIjVKFFpMKOs2oSle3ORp3U+7PxyPH2YEuKyzglhuHNIKn7dfxEyngPHcTCLIrTVJoh1OlzXJDgmC4NGKcPoLvFQyQWo5QLUch6F5ZU4X1KN/5vYHSnRIb55MYQQt6CExwPMFhE3zt+G/dllLdrPVd0TERUq1S748M+TddZcPkSGA8BwZfdEjO4cD0I87UJxFR7+cR8YA47laTEgLRrjuyWAManPGoOUTNS8U2vm4ZKWMds6xgC9Xg8G2OpxMGa/vm77mgzF2bqa59b/oJDxuLl/W7v1IpM6S5dVmVBebURhhQHniqWibCcLdPV2cv79UEGz/p1endodtw9Ja9a2hBD3ooTHA8wia3GyAwCrD+c1qf3indk49upVUMmpUzPxrK+2nMW+C2W255tPFmHzySKvHLvmZCcHafQTZ13GgYP1P4eRUHXb1rapXe8ppwsrPXsAQojLKOHxAJVcwLm3Jjldt+NMMab/Z7vH6pi88vsR9EmJtA2LNej14DhAE6qrM3RW6ntQU7jQZJFuepMIkyjijiFp0CjprUHq9+LkbghXy/GR3ZlHyVXdEzF7eHtkJGhsSQhQk4Q4JikVFVpwHIfw8DCpHTjnbX1wWddTI8gCWWGFAXnlekz55G8AwHMTu+KeKzr4OCoPWf868Nfb9su6TAamL/RNPKRF6FvNywZ1iMHZN50nQ1q9Cb3mrmnR/hftuIBFOy60aB9vrz5eb8JGCCANTX9sfAb+PJqPwxfti2OuPpyH1YfzcPbNiS4lKSbrGUmljM5M+qutp4tw/8K9KK0yOaxbui83sBOedXOBv9+XHrcdCESlAmY9cG4LUF3i2P7Y714Nj7gPJTx+JFwldynREOsUeDOLNZOLSgXYSiqNmPjR5hbFER8WXPOUEc/5/cHhqDJaUGk0Y+Drf9qWt4sOoVFLQaCwwoAv/jqN/2w+63T9oZcnBN7Z4PzDwO6vAJMeMFUBh5fUrsvZKd2i0oDwZMeE55kcQBnm1XCJ+wTYO5UA1lL64CD9MBZwulCHkkojOAB6k+jyfr6ZNQCjqJMzaQGO4xCqlMFotn/fqeS8jyIi7vT1lrNOk53/3tEf47ol+CAiN5g/AmCN1GAqPee47O4/KdkJcJTwBLhjeVpc9UHzzujsOleCzNQoyHjeNqkjz9OvctI0G44VYNY3u+yW3TMigC9xEJv2saFOlxfqDGCMBdZZvMITwKcDmretKgLQ0I/DQEcJj49tOlGIpXtzYLIwGMyirSOxhTGHeXvs5+6R7i0t6P386YbT+HTD6QbbBHWHROIWXZLCoFHKoDOYbcue/OUAnvzlANLjNfj1/mEIDbTLHkHo/345gB93Z7tlX88sOQizyHD74FS37K8hXOlZhPx2t9SvprwZ/RPVUQATAX3TpvaxM/RBIJIq3wc6+hTysTu/2un2fQo8B7nAQS5IlxUq9OZGtqhfZlqUu8IiQSopQo09L4zDigOX8NhPWXbrThbosPdCKUakx/koOlKjucmOQsZDo5TZpsXgeQ4pUWoM6xiDsiojVHLBo6UwFAcXQSg+0XjD+lSXAgqNlPgISkBQAIJcuuetX4FMlG6FRwFOANoOgK2KlDIc6DIFqCwGeEG6cdZ7XgZwPDxe34C4BSU8PrbpyVHYda4UShmPBxfvc8s+a2aubkp/HmcCskMi8boFW8/hpd8OO1236O5BGNop1ssREWcOvTwBPV76o8nbGc0iSsxGu2WFFQaMeW+T3TJPjew0jHgWxl63IiwsQkpKPhkAiI6jxRpkdF5Q0ilmAbK32y87tbbhbeQhwHOXmhYT8Tr6NvOx1JhQpMZI18nHd0tAhd6Mmm40FsYgijX3UhJT87hmVNbxvAr8sOsCOI6D3mRBxzgNlu7LdUtsrnw47n5+HGI1NKqrNeua5Fij5sPpfTCmSzzCVM2bS464n0Ypc0hKzBYRRosIk5nBYLHAaBZhsjAYzaJ0s0j3y/bn4odd9Z8hur5fG88FznFgkWlATS2kF+sUuGRMSoJEi5So1L2/fNmRZcCa5z0T4xgP7Ze4FSU8fqQ5p4Zv+c92u0tWB3JacJ26GaoMFkDj1UMSP9POyRxTQzrEULITAGQCD5nAAwoAqP/vNbhDNEakx+H+RXudrvdZDSWOq7281JihDwIXtre8js7Ed4GB97RsH8QnKOEJcKsfuQLL9ufi7dXH3bK/uVO6Yeaw9m7ZF2kdKvSOlxforF9wOV2oqzfZCSg3fAm83sLh9LEZ7omFeB0lPAGuTaQac0Z1wpxRnVBYYYBWb7J1LqwyWjD547+btL+5y49g7vIj+MfIjnj66i7uD5gEnfSEMGx/ZiwGv1lbePBCSRXS6hnSTAKLKDL8ssfxMnlAVmOXq6Qh5peP2Bp4L6BJADqOAeK7Se1I0KGEJ4jEhSkRd1mV5APPjoBFZNifZwADA89xOFWgw2srjja4r/mbTlPCQ1yWGKHC2zf0wlP/OwAAGPXuRhx/7SqaLiIIdHh2pcOymUPTvB+IO1SVOB+e3nUK0P4K78dDvIoSnlZA4DmM7lJbNKtfahQW7riAs0X1z+R855BUWEQGgQoREhf1v6yEgacmyCXeM/Nr+7IZn9/aD1f3TPJRNM0gisC6F4Ht8x1HdvWfDQy+H4jt5JvYiNdRwuMHThVUwGhm1hmhpdmigbqzS9e05GyPGWPSAAVYBypAGtHFwGxfNCJj0OkqwQCElFkgMgDW9W/f2AuiyDDti8uGX1ot2HYeC7addyn+a3on46Nb+jbvxZOg8dUW+ykINp8swriu8YFVjbcVyMouw6YThU5noa/5/OE4YMupImw+WWS37T8X7kX35HAsnTMMCpl3pg/h8/ZDcehHQKGoXehqNm0xAvvrmdl891fSrSEP7AZi0107FvF7lPB4idEs4v11J7D+aAEUMh4cJyUkh3K1jW/s5/SmRualIUHPYLbg++32VXDv+XY3AGDvC+MRHapwthnxgN+yLmLtkXwYjUYwBsjkMoii9HkjMmDd0fwW7f/wRS0ynl/lvA+PqRpY9zJQWWhdYE1MGAMu7pOWD7xX6kdTU8Dv3N/A8RWO++pzK8BEaLIWtyjeFvmkv3R/yw9A56t9FwdxC0p4vORCSSU+39jwNA7+qHNCGL6ZPQAynoeM5yAI0pxbAs9BzvM091aAu+fb3Vh7pGVfgI1ZsjcHd9PcWl5RbbTgITcVMG3IuK71zCuVtRjY8XnDG/89z7WD1HdmxhdoZFZQoITHSzrF+3aW3e/uGkjl/YmD5AjPjUbRKGW4snsCYjXKwJtoMkCpFQLWPHoFjl7SQl9dLc1mHxJim5fvH9/vafI+e7eNwLIHhrvWOHOWNNrJWAnUjBflOMBQAfzxLGCqAgbcI42CEq3TOdQtDlhTRNBsAA790uRYXXbDl0DPGz23f+KXKOHxorNvToTFWiXZZBEhioDOaMbC7eeREK6Cxbq8psKpwSzii7/OtPi4yRFKdEl0rIZLyMtTe+DlqT2avJ1FZPi//x3AL3ty6m2jM5ixZG8uluzNxdoj+ZgxqB0i1HL0aBPRkpBJIzISwpAer8GGwzn4ac9FhKiUCFEK0FY3b0694/kVrjfmOKBLPcPV+8+qfzvRApRdkG76cuCn25sWpO341vmteEGabJQ5mV4nJAboPLF5+ycBjRIeL+I4DjKBg0yQqipvP1OM6fV0Gm6Jh8emY9awNESGKKDVSn2EwsOoEBxxH4Hn8O5NvfHuTb1tyywig8FswY4zJZj1zS679isOXsKKg9JcQ3cOSW1WkkUapzOYcdUHfyGntLpJ203pnYw3rusBpUyAXODcfzau8ISUyPA8UHQSWHpfy/anigCufB3ocb19kkNnEUkDKOHxofR4DWI1ChTpjI03dgHHATzH4fONpzF/02nwHGddJi3neQ4ynodSxiO3rPYD8bNb+2FiIA01JX5p97mSekf91TW4Q4wXommdqozmJic7ALA86yKWZ110WL74nsEY0rGFf6/zW4Gv3dzhV18O/PaAdPO2ezcCyTQqNRBRwuNDMRoldj8/Hkv25uCxn7JavD/GpIlGLWhaAZQ5C10rGX9d3zaYM6oj0hN82x+J+BezRcS+7DKXkh1AGtq867lxDkUyScvFh6lso6eW7T6Lh3850qL93fKf7bimdzJClQLUchnkMg5HL1Wga1IYHh/f2bWh6W0ygcyZQN5Ba/8cPVB6HjA3PTHzC4UnKOEJUJTw+IFhnWKRHq9BTmk1mDVZ4erU3KlR9ynHcdAZmndNvrmW7stFVk4Z1j8+yqvHJf7t1d+PONRsykjQ2GbctjAGDhwUMh4Rajmu6Z2MWA0NU/e0we0j8dCoNOzN0aHSaIYoMlvdLgC2zxpYl3GcNAmoQuAhEzhbDZ4NxwvsJigGgL9OFGJa/xR0iHNh5mCZEpjyof0y0QL89Q5wal3923ECwPEwW6SyFzKZvPmXrEQLIJqlYfP5B13f7q61QMrA5h2T+B1KePxAQrgKax8b2eTtNh4vwKxvdnm0ou1zE7siOlQBkUnTUozpUs9wVNJqqeSO00d8f/cgxIfRfES+pJYLuHtoCsLDmzdg4cmfs/DznhyHZOeWgSm42dVkpz68AIx6Wro1oqqmH6Krr8NQAbzfA9CXNS2mwXOA7tcByjBAoZE6NytCmrYP4te8UyqTeMSozvE4++Yk7Hx2rMeOcSyvAjdktsVN/VNwQ2ZbRFEBOXKZOaMdS/MHYs0pYq++aWXWHslH33ZRTtf5jZRBTd9m+2dAcj8gvisQmULJThCiMzxBwCS2/BTPdX3bQCUXoJLz0r1MQFSoHDf3T3FDhCSYRajl+Pv/RmP4vzbYln295RxenNyNau8EsLdu6IW3buiFtKftqyD/6e+XtJVhwK0/1T4vPQf87x4g74DUf6iuqZ9JZ5tEi9QvR6CvxGBGf90g0CZS7bTMe4XehJ5z17i0j6X7cp0uf3HZYUzsmYjPbs1sUYwkuMVqHDsgt39mJbY+PQbJkWofREQ84cExnRChlvs6jKbZ9DaQs9P5upiOQLvB3o2H+AwlPEEsTCXHgWdHAABCNWHQmyyoNJpRbbSg0mBBtcmMGz7f1uh+RmZQhWbSMJVcwKjOcdh4vNBu+esrj+LTGf18FBVxt4/Xn8LjV3b2dRhNM+VDoN8d0pmfX2YDhcdq1301wb5d5kyvh0e8hxKeAMYYQ/tnVnr8ONMGtPP4MUjgKq00YvCbf8Jgdqxqu+98qQ8iIu40e1h7fLXlrO354Yvl6J4cQNWyBXntWZzxrwKLbnLeTk59doIddVoOcIPaR3tkv9MHpOD2wanY/fw4j+yfBA9B4Jx2cH3r+p7Y+oznOtQT73hxSje75wt3XIDZ4mTKhkBQX7Iz/hWg6zXS/F4kaNEZngBUaTDjwz9PwmCyoGtSOLom1Q7X/GbrObcc44dd2XhyQmenfTMIqVFQocfA1/90uu6GzLZejoZ4w6IdF7BoxwXb8+kDUvD6dT3rHdXlV9oOdN6fZ+2L0g0ABAUgKKX6QcbK2gKJKYOks0CKUGnYuiADsncBRcdr9zPxXUCmkkZ5dRjl8ZdDmoYSngD0+cbTbplUtDF76XIEcUJvsuDW/+7AnnreH6devxoygU4eB5Ndz43DkUta6PRmFFca8OKyw7Z1P+zKxg+7sh222fnsWMSH+1ktprvWAEUnAG0usPpZIKk3kDIAMBulEVwWI2DUAVWlQHUJkLsH0FkTnuwdje9/5RP1ruJvXQExoZebXghpDkp4AtBDY9ORHKmG0WzBsbwKpx82TaUQeFgYw1czB1AnZdKg0ipjvcnO9mfGUrIThCr0JoQqBCzZm4Nl+x3n3HKm2mTxcFTNUFUiTWuh1AATXgfObARWPC6t42UAx0tJjwdwFpNH9ktcRwlPAFLIeMwYJHUkLq00uiXhMVqvyc/feBqD2kdDIfDgA+EUNfG6pAg1jr16FTYeL8APu7LtRmYlhNMl0GCz40yx03nSPr6lLzQqGTRKGUIUAkIVMoQqZYgKkftn0ltVArzTof71sZ2BjCsBeaj1slXIZY9DpMtVMhUgt97LlNK9oGh02guLtWI08R1KeAKcRiXD1T0SsepQnlv2t+1MMbq8sNpu2dI5Q/2/sirxKpVcwFU9khCqlNklPO2fWYnEcBW2Pj2GEuYg0SbKvo5S2yg1Nj81OnCKSuoKgS/HA6Vnna9vNwSYthAIbeGs8MTvUcIT4OQCj89vq78o4I2f/Y3dF8pbdIx20TRckzg3vFMs5t3cG4/9lGVblqfV47vt53F9vzYIUwVYkTpiU6E34YVfD2HvhTK75esfHxU4yQ4AlJ2vP9kBgAvbgOztQBfH4q0kuFDCE2RySqvw8vIj1glFGSzNmHZi3WNXoF10KOQCF1gfbMTrOI7D9f3aIqe0GvPWnrAtf+m3w9h1rgSfUNHBgMQYq7dKu0Lmh5erapTnAD/dAVTkS/V3BOvcf+oooLqBQRj5RyjhaQUo4QlwjElJjVlkEBnDc0sPYdOJwsY3bMC4eX85LFt2/zD0Tols0X5JcNLqTeifGoXbB6fiu+3nbctfnNytga2IP8vT6p0u75oUjiqjGWq54J8/hrJ3SCOrmmLIA0Dv6YCuAJCrpYrMJChRwhNgcsuqMeyt9V4/blKknw0vJT5XaTCj+0t/OF2374XxiApVeDki4i5JEWq8cV1PPLv0oN3yo5e06PbiHwhXybD/xSv9r59WjxuADqOBqmLAUCHdsncCG16rf5ttn0i3up4vBGT0/g02lPAEmJhQBfqkRGJ/dpnb990lMQy3DGwHJl0Pw80DUhCioLcIcU4mcOjVNgIHcmr7iHGcNJHotZ9tgcgY5DyPO4akYuaw9j6MlDSVRWRY0EAR02v6JPtfslMjJFq6AcClLKCyoGnb97hRGqJOgg79VQOMSi7g1/uH2S1jjMFoEWEwi9CbLDCaRfx+4BLeWnWsnr04dyyvAi/9VltQLCunHO9P6+OOsEkQUsoE/PbAcJRVGbH6UB5eX3kUFXozCisMdu3mLj+C6/q2RUQIdWAOFDwnJbSX+/3B4ejRJkDm0Vr3MvD3vPrXT3wXiO4AdBzT6JByEhwo4QlgJ/IrcLpAB5FJw9MNJgv01qQnVCnD85O6okxXhUW7LqKkyrWiV5mpUeieLE1V8ci4DE+GT4JAldGMPedLka81oEJvti1/5uouSIxQIT5MhXYxIZTsBBiO4/Dr/cOQ/twqu+V/nyrCwh3nMfea7lDKBB9F56KGkp0B90h9dkzVQP4hQJMIpA4BImmi5GBGCU+A+v3ARTywaJ/b93tltwQwAAdyyvDSb4ch5zkM7RSLG/q18c9OisRnLhRX4Yp3Njgsv2VgCq7r2wYxGmVgzK9EnJILPI6/dhWmfrIFx/IqAMB21njxzmyce8vPRzW9WAoYK6T5sErOAN/UiXfXf5xvM7dlJTyIf6OEJ0CNzIjDpJ5JWHHwklv3+6aTy2BL9uViXNd4RIZQJz5SKzZM4bTo5eKd2Vi8U6r+3S0pHCsfHuGL8IgbKGUChnSMsSU8NeROLnf5HZ4HVBHSLTS+8fa3/OD5mIhPUcIToMJUcrw4pZvbE5769Hllrctth3aMwbezB/pneXniNiEKGT6/LRPv/nEcn2w45bTN9f3aeDkq4g5lVUY88fMBFFbokZXjeNbj6CtX+SCqJmAM2P0VsOu/ALiG++jc/SeQ3E9KkEhQo4QnQFUbLRj0xp++DsOpraeLYRYZ/P0SP2m5W/+7HVtOFTtd5/eXPEi9lmddxLqj+fWu7/TcKkwfkILHrsxAfJgflqww6oAVj7nW9r9j7Z/f+TvQns5KBiNKeAKMRWQ4W1QJkTFwHKwVld2D5wCB56Qbx9U+5qWKyzwH8BwH3vprySyKyNcaMKpzHObflgmVnDKc1uRiWbVDsjOldzK6JIahc0IYtp8phkouQCXnIRd4MCbNw0TvE/9hERmO51WAgcFsYTCYRRjNIpIj1XhwTCdU6M2oNlpQUmVEaaURu8/XViv+YVc2ureJwO2DU30TfNkFoLoM4AVANAMWMyCarI9NwHX/BrS5gNkAGHSAoRzQawGDFtBbH1eXAtUl9vtdMBmYtVrqxEyCCiU8Aeb//ncAv+zJ8ci+RQaIFgaTpWlZ1Mbjhdh9rhTD02M9EhfxT8mRarw6tTteWHYY0aEKlFQasTzrIpZn1b9NYrgK258dW38D4lXz1h7HpxtON2vbf9+eifFdE9wckYuKTgKf9PfMvse/ArQb7Jl9E5+ihMdP6U0WPPLDfqw/XgCjWfR1OPUa1zUez0zsio5xGl+HQnzg9iFpuH1IGgAg7ekVjbbP0+qh1ZsQTpOK+pzeZMHucw3ML1WPzU+NRoq3JxTWlwPb/iUNJTdWSmdx3KHLZODmb6WzRCToUcLjAyaLiGqTBdVG680k3fR1Hp/M12H14bwG96OU8eA4gIN0uYnjOOtzgOc5cADAGCoMFpibMYmoK3q1jaRkhwAA3p/WG4/+KJ3e6ZIY5jCyp8b208W4snuiN0MjkDoiZ+WUo6jCgOJKA3aeLcWOs/aXcxLDVVDIeChlPJRyHgqBx8l8HSoMUoIxIj0WGqUHvzZEEdCXWS81SfeKM1ug2tZATZ2wZKkysiCT7nm5/WNeJnVavrDNMVFKH0/JTitCCU8jDuWW43xxFQQeMFkYzKIIs0WarNNsEWGySJN3mi5bbhYZTBYR647mI7uk2u1xPTmhM+4f3anRdlqtFgAQHh7u9hgIqeu6vm1xXd+2tudbTxdhxn92IEQhoMposS2/97s9iA9TYmLPJOw6V4KJ3WJxdbc4qEJE/56JOwCJjOHzjafxr9WO5SZCFQI6J4RBZAwMwLybe6NX20ivx2iz4wtg1ZMOix26RAtKQBUORKYC0xcCYZQ8E9dQwtOAKqMFkz/e2uTtZDwHmcBBzvO2X0bO9EmJxB1DUqGWC1ApBKjl1pvC/l4lF6iAGwk4QzvG2o3Uysouw9RPtwAACioMWLI3B1q9GYcvavHOujNSm5euRISaLne5yxt/nMZPe6XSFWFKGb64oz+SI1WI0SgRqvCzGc8Tezgu63EjKoY8CaZJQnhEgExpQfwWJTwNUMmd/9qcOTQN943sABnPQy5Io5jkAg9ZnRFNhBB7vVMi7RKg+xfudagjFebJyyWtUE2yAwAHX57gw0hc8NuD9s+vfA3InAVm8N8+jCSw0KdLA3iOw87nxuJ/e3LtTgl/s/Uc9l4oxYGccrwytTumDUjx/3llCPEjFXqTXbIzJiMGX82mkTGtWrG1eOU1nwD9bq9dbtD6Jh4SdOiCeSPiw1T456iOmH9bJmI1CoQopMTmgLX66IvLDqPz86vx8+5sX4ZJSMA4V1SJnnPX2C3TNXDpl7QyWYt9HQEJUnSGx0VX9UjEVT0S8ezSg1i044LD+id/OYBXfj+CcJUcoUrB7hKXjOchE2oecxDqXAqr7znHAZtPFsJkYQhXyaCUCwADpO6FQM82kXh2YheavoH4pWKdAY//nIVinRGJESpUGy2oNJpRZbBAqzc5tP98upP+G6RFinVGu+fdX1wNmcCjV9sIvHxNd3Twp9GVC2+ufXx+CzA30lZQUNXjFujHv+Wz0EjwoISnieZO6Y6ruieC5zho9SaUV5ugrTZBZzCjQi/ddAYTLGLNiC1pZJc0FJ3ZRnJZRBEn8nXNjmPXuVKEqWQY1CHaVhWZd1IhubqyCjzPIdIss63neUDgpESM56XqyjxXm3BRHyTSUnct2I392WUAgIO55ejXLhIapQzxYUqEKGS2HwUllUb8c1gbyClxd7swlf3He6XRAsCCzSeL8OiP+/Ho+AxrJWypGrZKVvtYKROglPHgvTVYQnX5KFJmG0KuOLQYljYDgIhY65Bz61BzQVHnsVx6XvOYtz4XrEPTZSqaK4tQwtNUChmPKzLi3LKv5VkX8eDifc3e/sM/TwIenE5rweyBGNwhGgqBpySINKqk0oh+rzqfZPaLO/ojVqN0uq6mdAJxL4WMx79v6YH7Fh9yWJeVU46ZX+9yeV+9UyLxxe2ZiFDLrfW/3Px5MPEd4ODP9a5W/+HivFiN+edWaVi7TCklQTKFdC8oGp5glAQFSnh8aErvZEzpndxgm/IqE04USAXcai6RMQZYGINoPYskigwWJtUDqrmJjMEiAhWVlRAZoFCqkK/V450/jrsc351f7Wy0zYYnRqF9bKjL+yTBa+0Rx0KZI9Jj8d7NvetNdohnDWkfZRsZZxEZtNXSWelKoxkGswi9yQKDSbrXmy3QWx/naw2Yv6l2yoms7DLbZMXdk8Ox4iE3T66pjgJeLJXmwrIYpbmwRDNgMaKivBScaIZGraiz3NpONElzaF3+uLIQ+PNlx+N8PrThOAbeKyVfJChRwuPnIkLkGJAW7bA8X6tHvlYPJTjbD5OaqssAMPGjzV6JjwrFEQB4c9VR/HvTGdvzzNQoPD4+A2EqOQq0Buj0Zv/qM9IKCTyHqFAFokIVDbY7lqfF2cJKXN+vDU7m63C6UGdXOHL6gBTPBMjzAG89+1IH48KlnouuFE+1mIHDSwBNPDDqGaD0vNQXiOMAU3WDZ5EAAJ3GNzt84v8o4QlAxTqD7deWNwztGINpA1KglgsIUcgQohQQohAQIpdBRgURCYD8cr3d8z3nSzHjvzvsln09awBGd473ZlikiXJKq3DVB/X/WOoQF2qbO80vbfsYWDfX9fZPnJSSI9IqUMITgKJCFHhkXDrWHc0HYwBjUgn5yzEGmC3SLzOe58EgtTNZRBjN0s1kYdJji1Tcq09KpK2zaY2tp4ux9XRxo3E9OKYTRneJBwegS2I41AqqTdRafDC9L965qTfSn1tltzw1JgRquYDOiWEY1N7xTCXxL22jQvDq1O7462QRKg1mVBrMyLKW4ACAM4WVmLfmOOaM7gSV3A///x54H1BVDFQWWy9xWW+iCTj3N2Cqsm//bjow9CGpo3OXSUCbTN/ETbyCEp4AxPMcHhmXgUfGZTTatrlzaV3x9gZcKKlqvGEdH68/hY/XS8XDrsiIw7ezBzZpexLY5AKPh8emS53prRbMGog06uMVUG4fkmY7i3MotxyTP/7bbv1H609BIePxwJh0H0TXCEWIVKG5rrILwId9AGZxugm2fiTdb34PmFvuvA0JCpTwEKeWPzgcvV9e03hDK44D5HXqCU3tnYzyahPNi9TK5NW5tPXLP4ZQshNkOsSFom9KFGYPb+/rUFwnKJwnO2kjAGXND0EGdL8OMBulkVskKFHC00odz6vAzK934lK5Hm0i1VDIeKm7MwdwkKbV6JIYhmN5FS7tjzHAaBFR07fx8Z+zAACzhqXhpSndPfIaiP/5sU7F8cd/zsKmJ0f7MBrSEqsPXcI/vt9rt+xMYSXOFFbiRH4Flj843EeRuWjLh8DalwA4Xu4HAJy7rK/S8ZXAsRXAzQs8HhrxDUp4WglmHbZuFqU+PE8vOYBL1l/juWXVHjtuRkKYx/ZN/IveZP8r+uf7hvgoEtISepMFpVVGLHRSUb7GExM6ezGiJig6CTBRGrq+9kXXtqk5yxOWCIx43HOxEZ+jhKcVYIyh/TMr3bpPj9TiIAHtf3tz7J4PfONPzL+tH67qkeSjiEhzdHlhdYPr685471cO/AQsuadp22RcBcz40TPxEL9DRVRaiftGdmjxPhLClUgMVwEADl/UIu3pFbbb0Df/RHm14xxJpPW4It2xAnmbyBAfREJa4vXrGp7XbPWhSw2u95mMCUDG1U3b5sRqoIwmfm4t6AxPK8BxHJ65uiueubqrbdnpQh3GvrepSfvJ1xrqXXexXA+dwUydlFuhxTsv4JklBx2WH3v1Kv8cukwadOugVNw6KBV/nyzCzrPFOFmgw6pDtVW0//H9Xnw9cwBGd/Gz+jWqCGDGD9JjXSFwaT+gy5eqLleXSoUHd37huN0HPYCQWOC+TUBEW6+GTLyLEp5WyknZnmZpHxuKj2/pix5tItyzQxJwnE1XopLz6P3yGkzqlYRnJ3alqSUC0KYTBfjP5rNO1607mu9/CU9dmjig41hg2f1A1qLG21cVAflHKOEJcl5NeL755hvk5OSAt85aGx4ejgceeAAAcPDgQfz555+oqqpChw4dMHXqVKjVagBAdXU1fvvtN5w+fRohISEYO3Ysevbs6c3Qg06neA3OvTUJWdllmPrplmbvp1hnoGSnldv13DgUVhiwdF8u/rX6GABAb5IKWS7Zm4tr+7Rx24S7xHvqS3baRqnxxJV+2mm5LovRtWQHACJSgJQBno0niATqd7nXz/BMnDgR/fr1s1tWUFCA33//HTNmzEBSUhKWL1+OFStW4MYbbwQArFy5EoIg4IknnkBeXh4WLVqEhIQExMf78S8MP1EzMktnMMNskSorm0URZguD0SJCJRfw+4PDYbRIkwbO+M+ORvcZohAwtGMMTBaGj6b39cKrIP7KYLbgQnEVCisM+GDdCbt11/ZJxhvX90SIgk4kB6Jzb03Czf/ehp1nS+yWvzC5G7JLq6A3WxAfpoLgr9PLyFXAbUuA7693XJfQA7jiSUCuts6crpb68lSXSZfG1FE0e3ojAvG73C8+iQ4ePIiMjAykpqYCAEaPHo1PP/0UBoMBHMfhyJEjmDNnDhQKBdq1a4fOnTvjwIEDGDdunI8j9x9nCnV46Id9MJkZeJ6DwAOHcrUeOdb9ozvh/tGdPLJvEjiO51Vgwgd/1bv+qau6ULIT4O4f3Qk7z+60W3bfd3vsnm9/ZiwSI1TeDMt10R0AQQlYLut/mH8I+PnOhrftOgVQaIDMmUC7wR4LMZj4+3e51z+N/vzzT6xbtw6xsbEYM2YM0tLSUFhYiLZta6+dRkdHQxAEFBcXg+M48DyPmJgY2/qEhAScP3/e26H7tZ9253gswQlRCNj7wnjqgErsvLfGse/O7w8Op0ucQWRkRpxtGPql8mos2Zvr0Gfro/Un8cZ1ftrFILo98EKB/TJDBaArkJKe/CNA6TngwA+O2x5dLt1nLQ7IKSf27NmDPXuk5LSqqmnTBLkiEL/LvZrwjBs3DnFxcRAEAYcOHcLixYtx3333wWg0QqWy/4WgUqlgNBrBcRyUSvsOj0qlEgZD/SOGWqOnJnTGjZltbZeryqtNeG3FURy91PIkqMpoQZcXVmPjE6NoqgBi886NvTH60CVsOVWE3w9IQ5Wf+DkLqx+5wseREXcyWURUmywY8uZ6u+VPXJmB6FAlpvZJ9lFkl2HMOlGoWboxCyDW3KzLlv4DuLC1/n30uBGI7yJd0pKHSPvrGJjVwjMzM5GZKU2G+sUXTkantUCgfpd7NeGpm/n16dMHhw4dwsmTJ6FQKBxetMFggEKhAMdxTtdd/g/XGvx1ohB3fLWz8YYeEBkiR2QIDTkntSJC5LhlYDsoZbwt4TmWV4G0p1dgx7NjkRDup5c5SL3mrT2Bj+pM/lqfM29MBO+rvjtbPwHWPOe+/U39DEgdCoQlSf1+SKMC9bvcpxfYOWunsLi4OOTn59uWl5aWwmw2IyYmBhzHQRRFFBcX206F5efnIy6u9Y36uLx0v6ekx2uw9rGRXjkWCWxbTxfhsZ+yHJYfvlhOCU8AKqsyutSuw7NS5fYf7h2MwR1iGmntZoKbf3gtm+O47Pr/AL1udu9xgligfJd7LeHR6/XIyclBWloaeJ7HoUOHcP78eVx11VWwWCz48ssvcf78eSQlJWHDhg3o2rWrLfPr2rUrNm7ciClTpiAvLw/Hjx/H7NmzvRW637iye6JDWXfGGH7clY2nnRR+c9WNmW0xoXsiVHIeSpmAHm3CG9+IEEiTzF7u+UldUawzYum+HBhM0ug/nudwU2YK1ArqB+ZvRJHhl705OHpJCxnP467h7cFzgFlkqDZakKfV40JJFc4UVjps660fYXYG3SfdahirpIlCjTqAlwG8IF3eYhbpkpRBK42+qi6V7kvPAqbG+rTQCK36BPJ3udcSHovFgg0bNqCoqAgcxyE2NhbTpk2zZXqTJ0/GkiVLUF1dbRu7X2PSpElYtmwZ3n33XajVakyaNImGpFttOlHYomQHAH7Zk4Nf9uQ03hDAkjlD0a9dVIuOR4JHn5RITOiegD8O1/6qe23FUadt88r1eOqqLt4Kjbjo8EUtnvrlgEttR6TH4v+u6oLECBWiQhT+MSR977fAprdato+bvwVSBgEhMe4/gxRkAvm73GsJT2hoKO65p/6J3Xr27FlvASK1Wo3p06d7KrSANjIjDm/f2AsX68x4XreKssFgwPy/65/1uKniw1pf3ylSP5VcsEt2nLm5f1skRqjxDzfM50bcr2fbCHw1sz/OFlVhx5lirDlS/99z88kiaJSn8PltmV6MsBED7wEubAOO/Nr8ffx0B/B8ASU7Lgjk73IqkhFAGGP4fvt5LM+6JI3GEplUSNAi4mSBrsFt48OUsIgMxZWuXaOvz86zJWgbRRNCklpjusRj/bGCBtfTjOn+61SBDp+sP4XSKhPOFjletgKkyuyANGJrxqB23gzPuQvbga8muNY2oQcgKKQCg4ICUIZJI7DyDwNMBKqKgavelNaToEYJTwDRm0S8sOxws7YtqDDgloEpUMoE8BwHmcBB4DnwHHA8TweljIdccH56uuaEUVKEGpN7+ckQVOI3vpopleR/adkhLNhmX1NDJecp2fFT3247hxcb+Tz5v6u64K7h7aGQ8V6KqgFnNwMLJrvePiYduGUxEJvuuZhIQKGEpxFFOgN+3JWNdtEhYJDOsjAGMEj3IrMuq7NOrLO+Zp0o1rQBROs1J9G2r9rHqNO27vqafV/ftw3OFldCxnPWXu8MJpEhK7us0deyeGd2veu+mTUAozpTvyjSfA+OTYfOYEGlwYzVh6XZtfUmEdd/tgWDOsRALvBQCBxkAg+5ICXYhRUGdIpWYHSGl0f6tBI5ZXrsP1oGvckCvdkCvUmEwWSBwSzim63nnG4zvlsCQhQCHhyTbjuz4zUWM7D2BeDAj8DAewHRDGWVTpoXa9+XDW877GHgUhbQZTIQmQp0GgfwfpCoEb9BCU8j/vn9Huw6V+rrMMBx0rgBnuOkxxwHzrqc5ziEKqQzN7C2YwyoMJhd3n/bKLWnQietRKxGifdu7g3GGNo/s9K2/MglLfZeKGt0+37tIvG/fw61DXElLTf9q33Q6h0/B8JVMsSHKWGyiCitMtmW/++fQ5GZ6oNBCQYdsPld4OQ6IN86CGPjmwAAJQDGy6VigNX1fBZf/bb9yC1CnKCEpxHv3tQbI9/Z6LXjje4chy/u6C8lNoAtuWkurVaqtBweTkPNiXdwHIdzb01CtdGCri+uts2c3pi9F8pgERlk9VxaJU2TU1rtNNkBgP5p0bZLkT5TkQ+c2SD9Ovv7faDIcaqSGpxoAu77C4j0g/5DJGBRwtOI1JhQnHtrErR6E4p1RlhEERYRqNCbcOP8bW4/3objhUh/bpXD8hcnd8Ps4e3dfjxCPOGm+VvrPTOqlgtQyXnoTSKMFhHzru+KHslhSEuM8Y9hzkFi4ue76133wBg/mPz3o76AyXkn6cuJEangNQkeDogEO0p4XBSukiNcVTtksVjn3bm8Xvn9CF75/UiTt2sTqcLSe/qBzu8QbzhVoMO4eZsabFNtsqC6TsG63DI9RmdQsuNu39zeCzO/c15f5/rPnM8n9ev9w9AnJdKDUdVx53Jg+2fSaWyLCTi7qd5LVnz5eeA1J30Mb1oAdJkEcAL11yGNooSnmWI0ShyceyUKKgwwmkVUGc2oNFjs7quMFlgYgygyWETgXHEllu7LBcfZ18rxpNwyPQa+4/jh9vj4DDw4lkYvkOb7dMMph5mzG3PvFR2glPHQKGWI0SgxqH00ImSu9zUjrrOIDFd2icWR/EoUVhhgMDd+afHaT7e4tO+vZvbHmC4tOONSfBrY8JqU6FiM0gdiff1zGvLznc2P4XJpI4CZv7tvf8TvUMLTAmEqOcJUrheq6v7iagDeS3Ya8t7aE3hv7QmX2j5xZQYeGEPJEbHnyhdoXeEqGZ6d2NVheU0/M+Jedy1sWQX2hoQ34XPPqbUvAqfXN97Om6gOT9CjhMeLfn9oBP63JwcmUQQHabQVUDtrizTI6rLl1id12zBW25aB4YN1jc9u3BLvrjmBJftyIeM5CDwPGc/hdKEOVUYLhnWKQXSoEgqBh0LGQcbzkAkc5ILUbvf5Uuw8WwIA+OL2TLSNCoFCxkMp46GU8+A5zn9K1JMmeWx8Bh4bnwEAeHn5YXy95ZxtXce4UCRHqhEZokBUiHQ5eNqAFB9F2jqtf2gQdp4vAy9XgjEG0VoSo6aUhtnCYLKIMJqlvlRGs9jgcPUa/dpFguOA7JIqpEQ3swjpDV8C5/6WKhsLCgBMOtNjNkr3FoN09sdsQHWlFmAi1HIBEM3Wm8V6b5Lut37s+rE5XpoZnROA8gtA1ylAxtWAXA0cWylVbR7xmDQqjAQVSni8qH1sKJ6Y0Nnt+31oTDoq9Gb8tDsbRZUGjEyPg8gAC2PQ6SphYQxKlRqiyGAWGUTGYBGlm1St2f5Db+XBSwhRyJASHYLyahP+OlHodOJAANhyqtjlOO/9bk+96z6+pS+m9KaihoHqHyM72iU8N2S2xZxRftAxthWL1SgwsXt8k0dozr2mOywiQ5HOgItl1cgr1+P7Hedt/6/vvVCGGz6XBmw0u8+PXAWkj3Opqcl6BlDd0Ou48jXp3myQhrgbtICh4rKbFsjZBWQtBrS5tdseXS7d6tr6ETC3vCmviAQASniCAM9ziAiR454rHOcq0mql07Th4eGoNJjxj+/3YMupIvAcZ/u1V5/9jRQzlM74cLX3Am//nOdQUmmsd2hsXTRHV2D6ZstZzF3u2Jn+ziFp3g+GuI3Ac0gIVyEhXAUAqNCbnf64ufbTLVg6Zyj6+suEwjKldAutp5ClIJcSHlfMjQBu/ArocYP74iM+RQlPgMrX6lFtlEa6WOqesbEwGC0izBYRJgtDuU4Hs4Xhx31HsPV07QeW2IKORB9M64OpfZKpQBxxSHYyEjT4cHpfFFQYoNKboJIJUMp5qOUCvV/82JGLWuRX6G2XufQmCyqNFlQZzLb76/u1wfniKuw5b9+5ePWhPO8nPIwBuXukszaiRbr8VXN5y2IGmMV6icwAmPWASQ+YqgBTNdD9OqD0vLStXgvoy6VLaM78MpsSniBCCU8A2ni8ADO/3uWTY7d4dAYJKl/PHIC3/ziOo5ekyw4n8nW4+sPNDu3GdIn3faE74tSBnDJc80njo7NiNUqEq2XokxKJcLUc2moTnpzQGUM7+mBakN1fASsea9o2glLqpyMPARQh0iSi6mjprA8vk+51BUCedSh/+5HA9IXuj534DCU8AahfahTGdonH6UIdGACB43CmnlmO3a3mFDchADCoQ3SDE0vWrLtzaJqXIiJN1SUxHE9d1RnbThdLRY9PFTltp9WbYBZFtI0Kwce39EWEuoUjtVqix/XA2b+koexnG677BEDqqMzx0tkgU5V09segk2ZLN+uBm76W5t4iQY0SngAUrpLjy8t+LRvMFryz+jiO51fAaBaxwzoyqilOvX41ZAIV7yKuK6wwNDhxrdE6dP3Or3bi8MsTEKqkjxx/IzKGzzecbnTuPaNZGtTw14lCnMivwIC0aC9F6ATHA0d+db09EwFzdf3rt35MCU8rQJ8+QUIpE/D85G4ApA+mvRdKMf2L7S5v3yE2lJId0mSpMaE4+frVyCvXY8upIvxxOA8bjhc6tBveKbbBM0HEd5QyHlf3TMRPu3Ocrv965gBbPyy1QkBypLrldXhaSh4izYp+zIVCgdO+B3i5dQi8dRh8zXNFqDT8XBXp8ZCJ71HCE2C0ehOmfrIFZ91wCeubWQMwsH00FAJPyQ5pNrnAI0+rx9NL7AvdrXp4BLom0aQm/o7jOLx9Y29c26cNZvx3h8P6lQcv4cEx6WgX08yaO54gyIH+sxpPeHreDBxZJp3hYQxI7AEMfUjanrQ6lPAEiG+3ncOLyw43a9vEcKXdpYSoEAW6JYfjwUX7UGEw449HrkDnxDB3hUpaoVucnE2M0Sh8EAlprjyt3unyn/fk4HxxFX76xxAvR9QIg67xNgd/sn9+eAmQdgWQQh3oWyNKeLyEMYab5m/D7vMNzxeTHq+xVSKWW8+6MAbsPNf0Pjk18rQGAHWHXVbaxTHhg79w7q1Jzd4/ab0sIkOxzoA7h6bhy7/P2q0b+PqfTrfpnhyOX+8fZnt/E+8xmC147KcsrDhwyWFdckT9AxIeHuejqWU2v4fwP1+pfR7XVRp+brEOQQ+NAyodL6E6GPl/QEw6ENGWkp1WjBIeLxEZGk12AOBkgeOvlsEdojG8U+xlRf5qp3m4vNifYJ3eQeA5WExG8ByHULUKglC7noM0GuPwxXJ8eSd9ABBAFKUaTgZzbeVtg8kCo0XEwZxyPPnLAUzsmYjyahOKKowo0hlQUmVs8txwhy9qW1QHijSPRWTILzdg1UHHZAcALpbrkRShwoTuiVDKeWw/XYwQhQwLZg/0fP8rxoDfHwX2fN1wu8Kj0n1MOtAmUxpmLlNJxQYFuTT0XKaQlvEyoNN4IKajbYoe0rpRwtNC+Vo9Br3h/JdsS0WHKrB0zlCkxoQ2ex81EzM6Ky8/e3j7Zu+XtJzeZMG8tSfwxV9nfBpHqEKA0VqosjErD+ahX7tIpMaEIDMtCrEaJWI1CuRr9ZjYMwlhSjmUcukMpUouQCHw4GmeNL9w/8K9WH04r8E2l8r1DnNpZTy/qsFt9r4wHtGhLbx8eXFf48lOXcUnpVujnm5aHDGdgAfrnwKHBDZKeJppzLsbW1T7hudgN62DRinDsgeGoWOcxg3RkUCweOcFnyc7ADB9YDsoZTwUNTeBh1IuQCnULpMLPEoqDbgpM4USmAB194j22H2+FDwH8BwHnpPO6rSUW94NbfoB0xZKE4pm7wDiOls7GoswmswQYzOgCo+T5sD6+313HNE5qqoc1CjhaaJjeVq8uOxwo8nO0jlDIbd+Ych4ztZfQSnjEU/F+wik+aYiQ+TQGSzSbNYiAwPwy54cHL6odfvxPryxG8b3SgHP1V4CpekeWo/+adHY/XxtrZmCCj3+PFoAnd6MTzeeQlmVyeV9Teufgn/d2Mu9AXadLN0uo7eepVbVnKUeN9e+gSgCyx8C9n3X9GPKVMBD+4HwpKZvSwIOJTxNNG/NCexspKjf2zf2AsdxYAwwWxgYA0QmQuA5MJM0T41KLngpYuKveJ7DdX3b2i27WFaNl51MxtlcV/dIxOzh7aGvrgLPcThVoLP+uufA81KVbs76a1+wnrlRywVKyoPUiLfXI7ukgQJ8LvpxdzZ+3J1d7/pXr+2B2wentvg4Lln1ZPOSHUCqsjyvi3vjqYfbCzRcOx/oc4u79xrUKOFpoo9u6Yt+r65FlXXiTmee+uVAo/tR1RTykgtQKQTbY7VCgEouIEwpQ4hSgEIQUFJpwK/7LyJUIeDFKd0wbUA7d74kEsRWHcrDqkMN99uoT++2EYgMUSAqRI6oUAWiQhTIKa2CWi7gwbHpiNXQDPeB5or0OCzcccHjx+nTNtLjx7Bpkwns+q/3jucvknr7OoKAQwlPE6nkAo68chVMFhEVejMq9CZoq82oNllgERlExmAWGS6UVGHN4TwwBsSHK2Ewi9AbLdiXXYaSSiNEBpRWmVAK108jVxot+L//HaSEJ4glR6odSgQcy9OiQm+2jcKT8Tx4XjqTLzLpDKKFMetjBot1uciYrY2ushKMAUqV2rquThsmjdASGcOqQ3lYeyQfAJCVU15vnBUGM+bd3MeT/xTEA16/ridev65ng22e//Ugvt9emxTFhSnx3V0DEROqhErOI8zXVZYv12eGdKsrZzfw37H1bzP8UaDdUECQ1VZd5uWXPbdOKMqYVJE5pGVTaTQ0gIR4ByU8zSQXeESHKpyOTrCIDB2fXdng9jVzDNXQKGVQW8/0qOTSCBeVTIDBItrmKnpuYlfcPsRLp4mJ3+iS2PIPSK1WOhvT2Ift9f3aYthb65FbVv9lj0fHZeDuETTCL1htPV1s97ywwoAVBy7h8Ss7+yiiZoho2/D6smxg3JXeiYX4DUp4PIDnpF9FhRWGxhtbvXxNd9yQ2cj/pIR4UGmlEV9vPVdvsvPt7IG4IiPOy1ERbzKaRbw2tYfDFBMV+oYnFvWpsgvAhR1SIUJmkWZEX/5Qw9uEJUrFC2mKiVaFEh4P4DgOu56TRkMs2HoOL/3W+JQQ//37LNYcycOxvAqcL666bH/AE1d2xv2jO3kkXtI6jX53o0tzsj06LgNdksJQXm3Cvzedxqxh7Wki0CAhigxfbz2HQ7nlUAh8vR2Rv9l6DjOHpiEttvk1wTzmg4Yv0Tm17RPpNmkeMOAu98dE/BIlPB7WPbnxyxFyQRolc66oyiHZAaRLyO/8cRyzh7WHWkGju4h7jO0Sj/9eNh2EM++vO2H3vLjSiGcndvVUWMSLskur8Orvro0K1Kj89Ovitv8B3zejfo4mEeg4xv3xEL/lp+/gwHDkohYygYPJIsJsYTCLUrVas4XBJIowmUWYRYYPp/e5bL10bxKl7QxmC8qqTCirMuF4fkW9xztZUIFe3hz9QDyKMYblBy7hUlk1RAZ0iAuFWi7YOiLX7VzM7Doaw/pc6pTM0EAbUXpcrddDZAwKhdLWxpVkx5lHfDWvEnHJ6UIdrvt0C7R1LkO1iw6xva9qLlkOTIuGhTEoZLxDn8LLHXp5AjRKL35d5O4F/jMagJuHcz+XJ3VG5njrjepQtSaU8DRDeZUJvV9Z47b98RwQGaJAZIgcmalRYIxh74UyAMDcKd2QEK5CYoSKkp0AtzzrIrKyy1BtsqDaaMFfJwtRpDP6OiyX9WsXie/vHoQQBX1s+JvcsmoMe2t9vesvlDieOW7KhMQ9XvqjWXHV9fc97dH2Ox/PuP56ouf2fcOXQM8bPbd/0mL0yeWi77adwwvLGu+L0xRPXdUZCkGqxMxxHDhO6v/DAbghsy04SMvKq03Q6k04ma8DOKmUe007aRvY2totB4fq6ipwHIfQkEqgpo11e1ZnWHLNkHqLyOyG11tEZjsrZRYZxnVNQOfEMLf+O7QGZwp1eHDxPl+HAY6Tig3WnXC25mYrSGh9H/E8bMs4DtDqzbj20y3W55xtepS88mqUVZtcnkRULnBY8dAIZCTQ+8hdSgIgcVYYinwdgmfJQ3wdAWkEJTwu+ml3jtv3+fbq427fp6e988dxhzoxpHEd4jT4etYAnMirQIhCgFohg0rOQ+A42yWpuvVwLGLtZS1LnUtUNQkpcPklL9R/2YsBer0BImOQyxVOL5Mx1FwCc769/WU2BoNZxMbjhc36tzBZGPZnl1HC40Y920Y4/H+p1WqRW6bH1Z/tcttxHhrTya4ODwPDpXI9vt5yDkDtjzjGgBEZsY4lFebWX9sJALDpHWDDa26Lt9niuwE9b5IeV+RJ83qBwZbVcxyQ2AvodwddFgsglPC4aPmDwx2WsZovmprHgPV5zRdQnceXtUED65zuq87xUM+6Me9t8tjrn9gzEakxoZjUk+acaa7RneMxunO8T47t7qJnO84UO014frx3sG3CUaWMh0IQah/LaueWozm8vKNNpKrRHyiiKCXVFpGhywur623Xs00EbhnUDkkRaod1L03p3uJYAQAjn5Rudbj83t31X2DF4y07fnJfQFcg3fTlwKinAbnj6yWBiRKeFqi5DGV95rXjMsbwy54c/H2qCDzHYUR6LDgOuH90RyzPugSzRYTRIsJolu71poY7JLri/tGd0D05wg3Rk2AwqEMMpvROxvKsi3bLYzQKdIqnMzeB5EJJFd7+4xiqjBZ0SwrHkUvOJ649mFuOIW+u9+4ZXkMFVKsfBV9ZAMiV1mv11g7HTJQKDMpUUg0eixFIGwGc29z8412sc9l5yweAJh4Ycn+LXwbxD5TwBKAjl7R4ss58XUv35br9GOnxGvx6/zCEenNkBgkoV3ZLcEh44sJo0tFA8/SSA9h+pv4OzJ3iNeieHI6tp4vxnzv6ezEyAMdWQHHkFzfvlAM6Xw3wgpNpJWRA3kEgewdw9dvAgLvdfGziS/RtFoC6J0cgPV6DkwU6t+/7i9szcWV3D45kIEFjUIdoZCRocCK/9n3Y++U13h/CTFrk3Zt6Y9XBPFgYw1urjjmsH94pFnOvcdMlq6bqPR2VykRwuosI+f2fzduHQgM86/4fhSTw0KdSE2SXVGF/dhnCVDKEWYtw1XY0te80aqnpFOrQCRS16+qsr6/uil3HUmu/HZEBnRPDmpXw1AxzB6S+P4DUH0jggagQBQZ1iHHXPxcJACaLiCqjBRwHWCzWDtJ1+nTUHbVnEYEJH/zV6D4/23AKT13VxQvRE2cYY/h5dw4uFEodhFUqJfZdKMOfxwoAAGEqGSZYf9TsOlfitNhpXY+Oy2h6ENWlwI4vpMtOdW9g0tQPTKzTmVGs/1ZwFKG5e5p+/Lqu+bhl25OgQQlPE4x4e4OvQ3DJP0Z2RJhKhlCFgAtFWoTIBYSGqNGzTQSGp8f6OjziZp9vPI1P1p+0Pa87OtyWJIs1Szm7zu4WsW5r9/hs42ncOjgVbSKps6cvrDmSj6f+d6De9RV6M37Z4/qo02d/PWj78VaTENf9cSYyhpemdLcfdbfsAeDY7y15Ge7zyyzpBgAhMXUSKgZ0Ggtc929ApvRtjMQrKOFpgkV3D8LCHReg1Zug1ZtRoTehQm+GttoEQyOVSr1p/qbTds/fva4LbhzU0UfREHco0hlQbK21wnP2I2H/tdrxMkT93J/gAMCMQe0wMC0aRrOI9nGhlOx4EGMM648VoLTKBJ4D9CYRBrMF6fFhUMr5Osmte6w4cKnRNq8sP4LvZ/YF9GVSohPZTlrR/gpAUEqdjG0vQAROrXVrjC6rsp8JHoeXAuPmAlFpvoiGeBklPC46X1zpMIMwId5QpDOg/2vrfB1GgxbtuIBFOy402i46VIG9L4z3QkTBa/WhPPxz4V6vHnPGoHZ2JWgA+3GpmalRwGtxjhuebfwSqNeMeAJQWs9CZe8ELu6VOix/f4OUhClCgVt+BCLa+DZO4jGU8LgoRCFDzzYROJjbSOEsHwhRCPjh3sFOp56oqWFBAld0iAJzRnXEZxtPN97Yz13ZLcHXIQS8cd0SMKlXkktnXtylsWR24Y4L2C3chf+TLUYE13CfIJ/Z/G7jbd7v1nhxRBKwKOFxQU5pFa75ZAtKKv2zfHuV0YJjeTSxaLDieQ5PXdUFs4e3x6bjhXU6vTtWf6rtqWOvWq8HYwxqtbrebWq2q1v5ue4EpK+4OKs2AAg8B6W14KBSJkAplx4fvqjF1E+3oKjCYJvEskZGggb/++dQu0q+xJFc4PHpjH54dFwF8rUGALUFgBkYVh7MQ155NTY0sxJ2XQPbR4Pn7KcY4bmaqUhgm2KE5ziUc7fhWdxm2zbDcgJ3DYiFRiHU7pDjpE7LB38GKi4BZza2OEaXtMkEYtJra/gcXyF1rL7cw/X3fSKBjxIeFxzMKfdZssNbv53SYkJtlWoVgrVircDbPmzWHM7DuiP50gdTnTmQLGYTeJ6DUqEAz0lfRDXzbdmqPqN2lBhjgMEsQm+yoNpkgcHaP4DnOcwa1h5TeiVRlVwfidUocUNm22Zt645Ky1d2T8CRi1rpy46X5mIzmkVbH5IinRHv/CFNl2IRGaqMFlQZLQBMLu3/RL4OZVUmSnhc1Ck+zKHI4/pj+Vi8s/FLi67omhSO3NJq5Gn1dp3bJ3RPwHs393Gh9EA/54t3/gfIWuyWGAFINXRU4dLlKnkoUFkIVBbYt8ndI90AQJMIdJsKTHxXuqRFWg1KeFxwdc8knHtrEkSRwSSKOJRbjqX7cmE0i+A5Dt2Tw2Ewi9IQ3pphvXVGNNTcm+2G+NadrBO1j23b1z6330b61a03WSAaLdL8SpcNb7dYR04wBpwtqnTbv8O+C/tw9JIWT1zZGQJPSU9r0zYqBG2j6p8gsedlM2r3SYlEh7hQ6QyP7WwPD6VcgELgUVChh1zgwYlmZLaLwJgeKZ5+CUFvVEY8np/UFTml1dhzrhhtI1WICFUhMUIFvUn6IVPzY6baaLF9JtXc8iv0OFMofWYcrafi8h+H8/FStan5tZb63QFUFklJSclZwGA9TspgwFxtHbZuAUQRYBaYDHoAIuQCXzuk3Ta03WK/7MLWxo+vywP2fANc8aRUqZm0GpTwNAHPc1DyAjJTo5GZGu3rcPDMkgNYvDPb9jwpQgWZwEHGS2d+PDHbxecbT+Pu4e0Ro6FhnK2Z0Szi1325OFdciZ9254AxhgqD2a7N93cPculLkfqZuQ/Pc7h7RAcArp3VK68y4brPt9iSnIYcmHslQhWylv/YkSmB0c+43Lza+jrk4eHApQPAv0c0/9iPWUc0KkIAFU2V09pQwhPA6iY7AHCpXO+2fc8e1h6TeiXZfpUrZDxEBoSrZJTstDI7zhTjuV8PQeA4yGUcVDIBu8876f8A4J+jOmLWsDTE0xQTASFPq3cp2UkIVyLcHy415uxs+jZdr5EuYaUNB8KoinxrRglPADqUW47JH//t0WP0TomQhpqSVu+1FUdxysWq3rOHtUdcGCXEgaJzYhjOvTUJJ/Ir8OySg/Umslf5y3QzA+4GMmcDunxgngvVvBVhwLWf1Q5HJ60aJTwBwmC24Nd9udifXY6SSkOz9/P1rAHomhgORZ0zNzJrR2ZCnPnln0NwvrgKFlHqpGwwi8guqcLjP2fZtfPqLNqkRRhjOHxRi4O55Vh3JN827cTlNj81GinR9ffb8iizAVj/GsK3ftS07WauBNKGeSYmEtAo4Wkixhh+3JWNvRdKkRihRnKEytbxT7oXYbLYPzeLDBYLc2hn99xS20HZtr7OfvZnlzUpzuhQBSJD5JjeNxFXpEcjKSYKoUoBMoFvfGPil8TLOrGLTBoNVbdjvMW6fMupIizPuih1YhcZzGYzTCLD/pym9Zfp1y7SOkxdeu/XdJw3mi0ObY1mEQoZvb/8SYXeDLNghFkUYTRLt5zSatzxVeOXhjY+Mcp7yc6Bn4ElLZyZvO0AIKkPcHiJVPBwxOOATOGW8EhwoISniS6V6/H0koMut5fxUs0Kgecg4znIBN72uOaetz3n7ZYLPAelnEcIz2NkRhw2nXCtrsYLk7vhruHtAdTpuBjiB9ffW7F8rR6D3vjT12E0mVImQCZw1jIH1nIH1hosF8v0qDZJiU/35HDQwD3/suJQAZ757Xiztx/17kb3BdPYsfjj+KaluUnOLulWY9NbLdyhezW/IEQT3Llcms6DOEUJTxPdvWB3g+uv7ZOMt27oBbm1Rg5dKiIAoPCjM2txYUop0bbWbIoKUeCj6X2REK4CzwOCtbAcvXcDh0VkeP7Xg/hhV7atCKE/u1nYgDdl/4XABUCwgUQV6esI/BolPE3w381ncKRObYpxXeNxurASRrOIcLUcajmPR8dnQCUXGtgLaY2iQhXIevFK9H5lja9DQWGFfR+w7JJq/HE4D/eNpAlmA5XeZHEYtekpbunX8/aDQJWPkp22A4DetwD9Z9vPwuth7ij+SVqGEp4meG3FUbvnj1/ZGV2T6M1LXBMRIrd17N1wvACzvt7VyBYN+2RGXwjWS0w1Z2VqHtufqQGqKqX5jUI1oXb74ADIeB492tD7OJCFKmU488ZEW9FRUQTKysthYUBoqMZuuV1xU8Yw9r1NTTqW3B1nK588XafAYJ17MKn8O2PWxyIqtOUAGMK+GNDy4wLSZS9lGDDgLvfsjwQMSnia4OVruuOl3w7bnl/94Wb8/uBw9GhDBaxI04zKiMOaR69ApcGMCyVVePiH/Q5tpvROxu5zJfXWV3pg0b5mHTsxXIUVDw2nekpBhuc58HWqjZqsRR/DQxvuHLPyoRG4/vMt0JtEl45z+GI5EiNaWGeJ4wBBBle+gphoPdYTp4D8Q1JVZTCpEvOZjUB0e2lers3vuX780+ubEzUJcJTwNMGNmW3tEh4A2HuhlBIe0mQcxyEjQaoN0iclEhN7JsEiMpgsovWewWwdqffK70ew9ki+246dp9UjX2ughIdAFBnytXrMndLd5cEYb6w8ivfWnMBLU7phUIcYD0dYhyYO0IyufW7QAfu+BfIPSqOymqr0PBCV6r74iN+jhKcJ1HIBI9JjsflkkW3Zi8sO444hab4LigSs04W6Jl9OaKo1j16B9rGhqNJVgOM46j/QSu05X4oincFhtN2dLgxPv9xpa2XmaV9sR0aCBpz1rFJNd5iazu4cAK3eBKNZROfEsNpRqRwHmSCNShU44Kb+KRjWKdb5wQpPQHFgKZhcDYRoAE4AeAHIPwJs/7TJsdv5sBeQ1Lt2BnXbTZCmvxDk0qvg6szTU/OYq+d5A+3VJhMYxwNyhZS8jX4ekFNFcm+ihKcJeJ7Dv2/PxNC31qOsyrUZoAlxRhSb3neiOSZ+uBl7XhhPI65aKaNZRNrTKzy2/xP5rlXgLqiov1jqr/sv4vcHh4OrScSsSRnHAZ0+GwCPpgSXshpv4yYOhUGUEVI/Il4mJVe8XErm6P9Vj6GExwWP/bgfS/blIi5M6TDCZcHsgbhQXAWFjIdc4CCX8VAIPOTWejuEOMNxwKvX9sDPu7NxIKfcY8cxiwzj521ClFoGnucglwngrF8ogvXLpeaLpqaDc80XTs3QdL7OFxFnXV53u5r19m1hfW6/L44Dqo0W6AxmVBrM6JYUjlsz4yGj/1c84qQL82T52ih+H3r8Z4bt+SUWjTIWCjME6DgVNJz75gj0Kxtek26XS+4L3LvR6+G0BpTwuGDJvlwAjsN5ATTplPCMQe3wxnU93RYXCVwcx+H2wam4fXAqTuRX4Iu/ztSOuOKB77dfcNhmXNd4aJQyu1FZHAdb9WORMVtl5ZrHorU6stFkgsgAQRBsbWtG7YhM6i9ktMBWwVmsU825psKyVHHZfr+s7r6sbW37t1sPW5HCulYdykNCKI8JXeu5pEGaTas345av9ztdF6IQIBd4lFfXnqnu2SbC4W8uMuvf/bL3GCC1qcFgHVyFOssY6jyzDrxysr6PmGfXMIkrQRJXYhfvIdYBIngwcLZbCKrRHhdhAQ8ODAJEVEIFHdSIDVXYlwexxepkKHx965wWNGKNr6tnv6L19fOANCJNNEs3Zn1cY9A/nOybuAMlPC64Y0gqvt12vsX7GdQ+2g3RkGCTkRCGd2/qbbfstWvdmxj7ugbIphOF9f44eOrXY/huZxiWP0QVYt1pwfaceteNzIjD57dlejGaBvy2HNjbcJN2D69BeHRco7uKtN78kY7q8Pic/5R/9WOPjsuARtmy3PChsemY2qeNmyIiJLD8fbLhaVEOXqzwUiStR0Z8aL3rqoyOZ9t8ZvzLQNqIhtsw14bME9IQOsPjgqhQBTRKGXQGs93yJyd0xtQ+ydAoZVArBMh4mk6CEGeinNSCubl/W1zdMwkwGZAe56MZuYPUifwKPPnrMYflI9Jj8d1dg3wQUQPUUcC074F/1T9EnKsqAmISvBgUCUaU8DTiqg/+wrE8578+3/njON75o3ZyvtNvTKRkh5DL6E0WWCyOfR5+2p2Dn3bn4OpucfjXtV18EFnwilQ7nyy4Q2z9Z3186sQfDa7mTP7f+Zr4P7qk1QDGWL3JjjPLsy56MBpCAtPXW87hvbUn6l2fXRako3B8KD7c+WDuZyZ29XIkLsjZDVzY2mAT4WIjnXwIcQElPA3gOA7n3pqEEenSCJLkRsqpP/LjfpRWGr0RGiEBY9awNLx5fU+Eq5yfUK4ZDUTcJ7ukymHZoPbR/jexsV4L/HcssOebBpuJUe29Ew8JapTwNKKk0oi/T0mVlS/WM6fRC5O74Z0be+H3B4c77atASGumkgu4ZWA7PD+5m9P1R/N06PPm3/j9AJ0hdZe2UWqHZX45BY4qHJi2EIjuUG8TxsshhiV7MSgSrKgPTyPeX3vCVk6hU7wGpwrsK4te0zsZdwxJdc8MwoQEsZv7p+Dm/im256cKKjBuXu0cSCKd5HGbun0J59+Wiat6JPowmkZ0nQzs/gooOSM9f+QgEBoHyKWkrcI6nJuQlqKEpxGzhqXhu+1SDZ4HRnfCtX1paDkh7tAhVmP33NlZCdI8m+uUARjWyYsTfDZXnxnA6T+lxxEpNL0C8QhKeBrRvs6ohkd+3I8Nxwsg43nIeGkCvN5tI3FT/7Y0OouQJtpyusjueYjCz/qXBLCDubXTldzyn+34auYAxIf58USVqUNrHy+6GRj6ENC+kdo8hDQRJTyNuDyRWbbfvp/Bwh0X0KNNBLolU/VMQpri9i9rKy8LHNAlkf4fcpc5ozohPVqBexYdxKFcLQ7mlGNsVz9OeNR1qtCfXAMUHAMePei7eEhQooSniab1T0HvlEgAgEUUkRoTSskOIS10c78kX4cQdAalRdoej+3q50X78qzJjToauOkbIN55B3dCWsJrCc8bb7xh99xsNqN///6YOHEiysrK8OGHH0Iury2WNWzYMIwcOdLWdsWKFThy5AjkcjmGDRuGIUOGeCVudtlw2X/d2MsrxyWkNVm85xKGdb6Iyb1oNE6r9Pf70n11CdBhpG9jIY0K1O9zryU8zz77rO2x0WjEu+++i+7du9u1efrpp8HzjqOdNm7ciJKSEjzyyCPQ6XRYsGAB4uLi0KlTJ4/HXaSrrauz5lGa3JAQd2kTqUZuWbXteeeEMB9GQ3yq3SDg+ApfR0FcFKjf5z4ZS33kyBGEhoaiXbt2LrXPysrCFVdcAbVajbi4OPTr1w/79+/3bJBWZVW1Cc/zSw955ZiEtAbX9Kk9m/PUuA5Ip4TH7XomS/+mVUZzIy19LCZduu880bdxkCYLpO9zn/ThycrKQu/evR06BH/wwQcAgI4dO2L8+PEICQlBdXU1dDodEhJqr0EnJibi+PHj8IZHftxve/zG9T28ckxCWoP4MKXt8dvrzmDOOD+c9iDA1cxCr5T5+Qg4pTXZlSkbbkf8TiB9n3s94SkrK8P58+dxzTXX2JaFhITgnnvuQWJiIqqqqrBy5UosWbIEt912G4xG6QyLSlU7wkCpVMJgMHgl3rSYUBy+KBW+6hinaaQ1IcRVfx4tsD2e0jPeh5EEP4H387IZpurae1EEnFwKIU2zZ88e7NmzBwBQVeU41Yg7BNr3udcTngMHDqBdu3aIioqyLVMoFEhOlk5vazQaTJw4Ee+99x4MBgMUCmmqBoPBAJlMZnusVHrnl4DBLNoej523CTzHQeA48DwHbbXJrg+Cp8wcmoa513RvvCEhfooxhtu+3IEtp4qdrl9+sAAfezmm1iTt6fr7x/RqG4HfHhjuxWicCLde3jyxGnglyn5VzYNxLwPDH/FmVAEtMzMTmZmZAIAvvvjCI8cItO9zr6fRNae/XMEYg1qthkajQV5enm15fn4+4uLiPBWinfm39cOI9Fh0SwpHTmk1ThXocDy/Akcvab2S7ADA1ssKtBESiOpLdgDgnWu7eDESUldZlclhNKrXVRY03kaX7/k4SJME2ve5V8/wZGdno6KiAt262ddYyMnJgUqlQkxMDKqrq7F69WqkpaXZTnv17t0bmzdvRnJyMiorK7F3715MnTrVKzHLBB7f3TUIALDzbAlu/vc2l7ZLj9dgyZyhEHgOPMeB4wCe46w3x4KGhAQzjuNw7q1Jtud3L9iNdUdrv8AyEkKdbUZaoG4SMyI91vY55pcOL619PLfcbpXWOpdWeDjVO/MnAfl97pWjWO3fvx9du3Z1OH1VWlqK9evXo7KyEkqlEh06dMANN9xgWz9q1CisWLECH3zwgW3cvjeGsNVltoguJzsp0Wq8fE13FFQYrFNQSFNROEt6pIQIlBSRoMYYg9EiQm8UUW2y4JmJXewSnjYRflwFOEA9+1ttR9BbB7WDwWzxr87L1aXA+tcAgw448EPtcosJEOT1b0f8QiB+n3s14ZkyZYrT5T179kTPnj3r3U4mk2Hq1KleywIvd7GsGkPfWu9y++ySasz4744WH/ebWQMwqjN15iSBb9q/t2PnuZJ611Nu734rDtdOIPqP7/eiT0okfr1/mA8juszmecCu/zoufzUWuOFLoOeN3o+JuCwQv8+pK7wLHli01+55ZmoU0mJCEBUiR4RajjCVDBqlDGq5AIVMOpvT0kERHeJC0T05omU7IcRPzB7e3vaY4xwTnPf+POvliILfZ9NqBzq0jVLjqQmdfRiNE2NeAK77Arj2c2DE47XLI1KAlIG+i4sELZpLywX3jOiAfy6sTXr2nC/1+DHPFFZiwOvrHJaPSI/Ff+7oD5Xcj05NE1KPj/48iXlrT9gtc9Y/dtHui3j1+j7+P3w6gIQpaz/ec0qr4eNuyY5kCqBtf+DjfvbLxzwvXdYixM3oDI8LKgz+U6V088kiaPX0YUACw+8HLrrU7pObu1Oy42a929p38s0t9c6o0iZRRwGaRPtlS+8D/k3T+BD3ozM8Lpi/6XSztvvfP6VRWoK1c7J0kzom14zeEnhOugTG19b3EWyPARnPUwdmErDWPCpNGMgYg94kouuLq52265FERT09oU2kCrllepx9c6J/foaERANPHAeO/g78eGvt8uS+vouJBC1KeBpxIr8CZworna6bPiAFgP0pegYGgecxZ1RHpESHeCNEQvwex3FQKwR8PWsAft6djZUHa+tw9G4ThuhQhQ+jC045ZXrklukBBMAPJk3tVANIGQxc/x/fxUKCFiU8jZiz0L7D8vHXrvKvoZ2EBJDRneMxunM8LCJDx2dXAgCycitQXm0ClVlxr3fXnfF1CK4zS4kZhj8KjJvr01BI8KKEpxEFWr3d87VH8qEQeMgEDjKet12OAgBRZLAwBpFJp/CHd4qFTKBuUoTsvVCKKoMFAs9BZAx6k8Vu/aFLOqQkxPgouuD00sR0rD8hVbfu8dIfyHrpSv/tJxXRVrr/+31pItHQeEATD6ijAVVy7eSihLQAJTyN+OzWTNz2ZW1NnQcW7XN523uv6IBnJ9IM0KR1W33oEv7x/d4G2wxKi/ROMK2IRll7JlpnMGPCB39h3WMjfRhRA6LSah//+YrdqnAA2seyvRoOCU50+qERw9NjkfXSlU7X9W4bgb7tItE/NQoD20djSIcYDOsUgxHpsZjQPQH3XtHBy9ES4n9Gd4nHbYPbYUgH52dwdj81DDJ/PfMQwOQCj1/vzbQ9P1Wgw7+bOQDD4zgOeOQgcOfvwI1fO6wOn5cCLP2HDwIjwYTO8Ljgzq92Ol2elVPudHmNPw7Xls7vEBeKlQ+NoPo5pNVRygQkRajx/fYLTtf3f3sL1j04kPrwuNnqI4V46tdjdsv+OlmI+0Z29FFEDbCYgA/qr84LAMht+CwhIY2hhMcFD49Lx6yvd7VoH2cKK/HZxtN4ZGy6rc8PIa3FheKqBteP+3in3eSipPnyyvV4e/UxVOkNtmU39GuL16/r4dsfXKIIGHXA/oXSPFrKcMBYCZgqpfm0aiT0BG5eACg0gDoS2irpddDkoaSlKOFxwfBOseicEIbj+RW2ZWq5AIbaDsomS+N1TD/68yQ++vMkVHLeVptHJvB2tXpkAodKgwVFOkOj+3OmT0okvr97EDRK+tMSzxBFhq2ni3GqoAIygYdFZDCLDBZRhMnCbM/NFtH2WG+2NLjPG/okNrieNG7Z/lw8/MN+p+v+tzcHFXoTZIJU/6tmsIWMr1MjjOMg8DwEHvb3HOd0O57nwEG6VHbroHboFK9xHP7+24PA3m+b9kLCk4E/ngM4HuA4qC2i9FiusC2T7nn757h8+eVtXWlXZx044OhvQO4eICwJuGWx1NdIEQYI9PkaiOiv5gK5wOOPRxuu/MlYzYe8NCv011vO4ttt51Hzvz/HcUiOVGF4p9g6XxC1Xw6iyPDj7pZ3zNufXYazhZXoEBcKgedgsojgOQ6MMf+vxUH8mkVkuHvBLmw4Xth4Y8D2ZSoXeJgsosP6YZ1i8MG0vlCy5iX3xF6Yyv7jnAPQs00YOI6HycJwoaTK9llT9/PHwuo8drLcFd9sPWd7PKV3Mj6+xVo4MDbD+QbKCCCyHSBXS8kFE2tvunzrYwYwEbzFJD3mYF1usRY/Y7Y2tTcnz3H5ssvauTLpRsUl4ItR9a9/sRTgqUusv6OEx004joNc4CAXADUEPDIuA4+Mq+d/9nrEhSnxyYZTLY5lyid/u9Tu/tEdMXNoe4SpZNS3iDRKtCb19ZHxHA69PAFyoWnVwbVaSnjcYUyXBLvLglqtFkDLLgUxa5kNsyhCFGvvj+ZpMf2L7U63iQ6R1z4Z+qB0a4FKN7yOBtUkP7u/AlY+4ZljEL9ACY8feWJCZzxhndFYZzDj6CUtjGYRVUYLLCKDaP3FJdb5RSY9hlT/p86y11YcbfR4n244jU83OI7aCFPJEKJwTIDEmh9UjOG+kR1w7xV+2PmReIxc4PHdXYPAGMNPu7Pxf/87aLfeLDK8v/YEIkLkCFXI0C46BCMz4qjPWoCyiAwXy6pxMLcckSFyPPzDfhRWOCanL0zuhmqjGZN6JaN9bKgPIm3E4aVAVQkQmQqIZuD4Ctcvs039VJrvi4lSbaB2gzwbK/EoSnj8lEYpw4C06GZvf/eIDrhYVo053+/G/hxtk7at0JtRoW94wtQ3Vh6jhKeV4jgO0wa0g8DzeOLnLLt1//7LsbovdUYOTHMW7rEbaerM1T0Scdfw9l6KqBlKzgA/z2z6dj1vAjqNB2I6SpfceAEQLcD5bUB8FykJIgGHEp4glhypbnKy0xT/Wn0MT17ZmX7Bt1IJ4UqX2lH/scB094gOjSY8qw7lNbje56LaA6pIQF8GtMkEzAYg/1Dj2x38WbrVZ27DJUmIf6KEJwicKtDh5n9vQ5VROivDgYM3vl8+33gan2/000JmHnbPiPZ4blI3X4fhM1nZZXhoceNVx8+84aezdJNGDUiLtp2dY4zhvu/2YM0RxwTon9/vwfvT+vhnP0COA54+b7+sPBf4oIe1w7ILknpLbTkekIcAwx9zf5z/z95Zh0d1pX/8c+9YXAgRSPBAgru7FShtod6tu3cr23brbttuu9XfVrburlAoxYu7OwRJIIGEeDJ27++PO5EhM5lJMpZwPs9zn7ly7jlvmGHud855RRAQhOBpAczfkUtBmSXYZpxWRIcZPDdqofy+5Si3nFJU91SemtGTCwamidm/FoIkSbx9+UB+2JBdZxnz963H+H3rHPY/d2bzeL9jU+Hxk7B/MXxyTv1tu02Diz4BWS+isFoAQvCEAEt2H+dKN9mcWzI/3zaSvu3ivG7vi6gTQcMpqrDS98k/PLZ778pBTO6RHACLBIFAUbQUG2argtlmx2xT6NculnevGMjtX2zAckqqgc4Pza7e3/zEGcQE6kdByTGwVtSEmSt2R+h61b5yyjWl5vrl32tOzRs+c9337t/hmcS65+Paw+1rQe/dsq4gNBCCJwQIdxERdTow461lwTYBgD/vGUt6UlSwzQhZdLJEpFFHmaX+5IE3fLIWAINO4t4zMjDpZfYeLyW/1MJblw5oHr/+WzjL957gqd+2o6gqkiNLmORIISA59rNOlHl8r+sjNS4cQ2NnQxQF5j0Km77SjEEiCrTlJJ0BRzKemvZFQSoqmtIHdMbgjC1oNELwhABVa+VZJ8rYcPgkVpsj3NwRal6NJHHqI6PKPaL2lapzX6w6xJZs4VznidNVcHpLlEnPtqemklNYwfpDJzHpdVRatV/8FpvCQz86h6db7SrP/+5cw2n8y4uYd/dYjHqxLBBMHvxxCwc9lPnwlrHdErlrUldMeh0mg0y4QUdKTFjThK2lFFa86XQq6J+YW1dpMzmKHVp1FktbzRgheEKIjq0j6djIPBZWu8KaAwUkRJmqc/Q8PbMXxSWlbMkp4aU/64YL+5JAhB6LJa3g0jYunLZx4U7nyi02RqW3ZvvRYm7+bJ3bew/ml7Mlu5CBHRqfakHQdObeNYas/LLqH0iKWpMd3mbXSoNY7YpjU7ntC/e+Wot3H2fx7uPM7NeWVy/p7xsDw2LgoaNajS1HFuSS4iJQbESHm05ZqqraV9ycP3VZy64tfzU0ueD/DYW0IXD9PN/8jYKgIQRPM+fTFVk8+vO2gI553oBUZEnCrqgkRZu454yGZZQWtAwKyiwMeNrzQ+DqER3525D2ZKREB8AqQX2EGXRkpnj/gyEqbAhP/rqN4gorJ0pdB0Z4U0ewQRgjtM2Bqjr2m/pDp7IIXurauHvj2jdtbEFIIARPM6dNbLjnRj4m0qjn6Zm9Aj6uIPjY7Arv/3WAncdKvC5wW1huEWKnmdI1KYrcokq3Pj2fXjeEoZ0SQi/X0nNpYCnx3M4dPc+DiFaw5n/asSkKju/Wlrb0YaA3gs6k+RXJegKSB0TQZITgaQYoispPG7MprrDSoXUkJp2MomrlJHQ6ifevGoSiatPTiuKo4O6o5F5WXo6qQlhYWHWbqvo4FpvCF6sOOVWB94ZpvURl69OJV/7YxesLGlfjrW+7OF44v4+PLRIEiuMl5nodmK94vya6NMqk56fbRpASG06UKdiPlibOOm37wfl43Ufa5o6zX4eBVzVtTIHfCfan8rThZJmF79cf4XiJGVmW0MsSsqRVk67a1mYV8OeOvGCb6pF9J8oYkd462GYIAsSwLgmNFjzf3Twcg044eTZX+raLI+uF6RwtqqDMbGdNVgGP/rTVZRHZUrONSa8sAWDPs9OC+74/lK359tjNYKsEmwUUK9itWj0tu0XbP7YFfrm9aWMZo6HjKN/YLfArQvD4GbPNzo2frGPx7uNBGf/v4zrSPj6MqMhIh7CiRmhJUrX46tg6UjuWJJBAlrR2Uq1XCa0ifEhNXQv8TseE+h3p+6TF0rNtLFEmHYcKyrlzYjcSo03ERxjQC7HTbDmYX8atn69nW4735WkkCe4Yn44+0CkISvPg3430z/EGfXhNXa20wTDtRdCJx2dzQ7xjfqa4whY0sQPw+qIsr9rdNKYzD57Z3b/GCJolbePC+eL6oew9XkpBmYVX/9zjdH3zkSI2H6lJfzB3Wy7Pn9ebvw0Rjp7NmW/XHvFK7KTGhfP9LSNIiQ0LgFVu0JsgviOczGp8H7Jem/1xRWI3uGlJ4/sWhARC8PiZxGhTdci2za44cuuATdHCPm12xRESqnK81MyFb68Iip3vLNnPzxtzNB8fYED7OP51fh/iIkRyLQEM6tiK9OQo3nNRDd0V0WHiq6W5848zujGzf1se+nErqw8UuG2XXVjB0j3H6ZUai0EnY9LLGHQyRr1MfIQhMDPCYbFwp3PJC/J2QMlRmPMQHN/huQ93Ygfg6Cb47AIt2aBOD1V5z7qeAZ3HaTM/jkSJ1a+o2tKZYoO4Do36swS+RXwr+ZGFO/O45qM1wTbDa44VV1bvz92Wy5m9jzOscwKSBHpZRnZkZI0NP33rSJ2udHvk9wa1f/q37Uzv3UYsfzYDbHaF53/fyfJ9+dXRVka9JlxMetmr/+/3fbfZ9fkpGdw2Pr1J9knl+YT/dA2c2OkQFnKNwJBkkHUg6ZyvybLzcWImHN/pebD62OsiBcP2n7y7d9itMOLBpo0vaDJC8PiR5p5K/86vNtZ7fe5dY0S48WnCqPTW/LX3hNftc4vN/LX3BKO7uqhDJAgpsvLLef+vAz7py6iT0es0v8DkmDDO6du2yX3K+bvRH9vgA+v8hM5I9cyOJGv7ss4xG+SYERp4dZCNFIAQPH5lbLdElxmIiyqsqKqqLWkp2nKW1a6gQnXIuFoVZq6qqKceu2lns6tYbNoSWUGZhUd+2urXv2/Kq0u4eWwXHpiW6ddxBMHns+uHAlqY8uBn//TqniveX809k7vx94l+dCYVNJn0pCiWPTCBk2UWFFWlwmKn3GLnr70n6hVCf94zhvQk///gsbcbTvGd+4gJc/jY2K2nRFxVHdtqnbdqBUU3fQU2s1ayoqLQu6Utd7TpBzctbvz9xd47fwv8gxA8QaChS0KqqmK21VQt/n59Nv+ao03PTumZjKLCvO25TbIpMdpEZko0etk5VF52RG7tOlZCudWG3a6SU1Sz9DU+Q/yCP52o8kmzKyr//H4z3607Um/7r9ccFoKnGZAaF07qKWVD4iONdQRP79RYVFQuHdIhIGKnGp0RwhuQaXn7z/DNle6vpw2ByERtVmbnb1r/+jAtkaCqOspTqDVlKS7/ToSetwCE4Akx9uaVsGJ/AXa7QrnVzotzdtXbfu62pgmdKj65dgjd24gaVQLv0MkS/76wL49O70Hfp/5w2aZ1lIkl948PsGUCX9GvXRzLHpjAyBcWVJ/bkl0UkLp5TUJV6xc7se3hmt9FWPlpiHjHQ4yqxF2+QJJgTHorZEnCZDBoMzayhE7C8eqYwZElPl91sDovj06SUKF6OQ20WSZtKU3LLVRQZmHdwZNcNLgd952RIfKtnKY89dv2OucSo02EG7QK9OP/vYhLhrTjlrFdhANzM2Diy4vYd7ys3jb/+EaLhtLLEuFGHWnx4VwzshO6YPssntgDbw7y3K7oEHw6U5vNqR1VVf16asSVg9rnzSWwb752/o71Wo4eQcgjBE+I8dl1Q5m99Wj10pL2Kjsf62pEiV3RfHcqrHbeXrzPqa8wvY7cYjOKCrvz8v1i7zuL93P1iI5BqeklCD6PntWdzomRvDS3ZibyeIlzja0X5+zi+lGdMeqF4AllVFUl3Kjz2O779XWXMdvEhjO9Txt/mOWZ0jxY8hLsW+j9PVlLfTf+GwPgiSLP7QRBRwieEGNU19aM6tq4sg2unIeLi4tRVZW+z//VVNNc8rch7YTYOY2JizBy2/h0bhufzn8X7av2LTsVo17MAIY6kiTx2x2jAdiaXcRZb9T/nXHnxK7EhhtoFWnkzN5BrK+38r+w+l3f9XfOmxAep+2rKqDWfVXsmu+PMQomPem7sQV+RQie0wBJklj3yCQOFZRjU1TKzDbeXryPlfvdJxPzloxkEZYu0LhmZEeOFlXwyYqDTuen9EwOkkWCxtKzbQxPz+jJoz9vc3k9NtzAGT2T6Z4SE/z0G+MegLRBWqZknVETI7vnwJr3GtefrVJLGCgbtGUvnaFmXzZoTsy2Shh0HXQcreX8ETQLhOA5TUiIMpEQZao+HpeRxF1fbeCnjTlN6veJX7dTUmnjDhGJc9oTZtDx1IxezNuey9FakXzvXOGFX4UgpJAkiegw99GkRRVWpr9eMwP07c3DGdyxVSBMq4veBJmnOFLPfajx/c2+1/u2Y+6HCQ83fixBQBGC5zSlqMLKTWO7cNPYLlhsCsdLzMzfmYtdUflmbf2hxqey/0T9To6C04v3rhzktByy5UgRvdNig2iRoDHER3pfViY5Ooh1tFwRHu+7vqJStDD1qoSC0W3AGKktew292XfjCPyOEDynATZF5bz/W8b6Q4U+7Xd8RiLn9GvLzH6pPu1X0LyZ+dYyp+Mt2ULwNDe2Zhdx1QerXV5rExvG/H+MJcIYYo8Pb6O0vCF1EPSYoW3xog5WSyHEPrECf2BXVJ+LHYCFu46zcNdxDuVXIElw+bAOtGrAr0JByyQp2uSUnPKTFVlcOlRUTm9O1F6SdHWtx2NzAXjsrB5cO6pToMyqH3OJ7/rKXqtt8x51Ph+ZCBlnOup16bUZn+Re0OMcQNJmgmQDmKJ8Z4vAZwjB0wJRFJUFO/Ow2hUqKysAePvygVqpl1rtZEnC7sjiXGmxU2mzU2m1U2lVqLTa2ZpTzJLdxz2O958/dwPwyjztNVAp5wWhSY+2MU6CZ+cxHz6IBAFhYmYSz57bi+X78okw6Cg127DYFObvzHNq99Rv2+mdFhs8/53apLoID68sgnmPa+Ul9EZNjCg2iGuvOSZbK6A4B3b8CrYKLduyzb3Yo+w4rP+47vlfbnc+vm01JGY0/W8S+BQheJoZ+46XMvHluvVchnRqhUEnYbWrrD7Q9OirppBbbBaC5zTCZle48dN15JdZqLDY2J1b6nT9ulCZARB4TXZhBQ//6F0tvuiwEHqMWCvg/TPgmOvq7fUiyVrYuTFKm72RJKoLGZobkGenVRdNUAlCjhD6pApqk3WijAU789DrtGzIVQVDF+1yPePSGJEzumtr/jk106l2lk6qW0tL78jQrHdxTnD6oigqW7KLmFHLZ6dvWizXjuyEispNY7qQEhtizqwCr2jXKoIPrx7MNR+tcTq//IEJtI0L4bxb5tK6YscYrYWSS7K2hcXCRR9pwqQq1FzWi/Dy0wAheEIMVVVZsT+fS99b1aR+ltw3nvYJERQ7KvTGxIg6WQLf0vmh2XXOfX3TcMIMnrP1CkKf8ZlJrH5oIkOem1997v7vNvPZ9UODaJULVBWW/hvWfAAlLtJsPNSwqFNBy0UIniDy6cqDPPqTd9PGnrhsaHuePbe3T/oSCDyxYGfdorW3je9CucUuBE8zR1FU/rt4H8v3nWDZXueSNH/tPUHHB2YBcMu4Lvxzat3s7gGjLB++vlxzLrZb3Ld7olaEYOsMuGW5KBx6miLe9SBSXGH1ST+tIo1C7AgCRkmlle/XZ9c5/9bCfby1cB9//XM8afERQbBM4AtW7s93qo3mjs9WHuTeMzICXzR0/2L45JzG3XtiFzyTqC1xTXkeYtNqlrokWTtO6eVbewUhgxA8QaSqBtHarAIueHtFo/u5ekRHZm85ilztf6NFYMmSRGVFOTpZIjrKhlyrSnqVL44s41QlvaqieoRBR1SYHoOogi44hd5P/FHv9bcW7uOKYR3o0VYsozZHMlK8CzgoqbTR5aHZbHh0coOSFDaZVp0guq3r5StvUBXtde6Drq/fsgKSezSub0FIIwSPnziYX8bkV5ZgsSt+H6sqHNwfrH90ssitI6jGmyXYL1cf4svVh9jz7DQhmJshCVEmsl6YjtlmJ+OROR7b7zhWzLBOCYELYohrD//Y4fqa3QpPN674MmlDoMsEEU7eghGCx09kF1Y0Sex0ToxEL0soKiiqqhXoVdXqYy1aUnVEb4GK4xzaeRzn7YqCdiRVt6/qS5YkLTePJCHXegUJs83OtF4poRVyKgg6n6486LkR8Pn1Q4XYaeaY9Dq+uWk4F71T/+zzpe+t4oweybx7ZQjUTNMZanLxfHwOHKibwsMtR1ZrCQTnPgw7foGzX4Ouk/1jpyAoiKeZnxjRpTVZL0znm7WHuf+7hueEMMgyc+8e02Q7RJSWoKmoqorVrmKxK7xzxUBu+nRdnTZrHp5EYrTJxd2C5syQTq1YeO849uWVopMl9h0v5ZlZdWdXSs22IFjngSt/1hIPlp3QXvf8AZZSWPGm+3v+eKRm//ML4Jw3YcAV/rdVEBCE4PET/5m3m9fm72n0/bHhBt5auLf6WHXM6gAY9TKXDetAlEm8fQL/smhXHld/uMbt9ck9kkmINHL4ZLkQPC2UIyfL+d9f+5HQZoRHpifUid4qs9iDZN0pWMrhlzsgZ4N2LEmABPmN+C5uOwC6nuFT8wTBRTwx/cSADk2r1rs6q4DVWe6TCT7/+04uHdqeMV1bM7VXmyaNJRC4o0eb+mcG523XwtO/WnOYrBemB8IkQYC54n3XRURrs+lwof8N8YbcrbD1u6b3c/n3kD6p6f0IQgohePzE2G6JZL0wnUqrnW05xdz4yVryy+rJFdEIvlh1iC9WHWLLE2cQHWbwad8CAUBSTBhfXD+US/9XfyLMF8/vw8kyC0a9jEEnY9BJSJLIxN2cyCuppLjCikEno6pgU1S+Wn2IG0ZrpUE2HS5y+yPskendfWeIpVyrWSXJgIpUXIik2KAiTCvOqdgdr4rzsapo++e8oS1hVZ23VsCSlxpmQ7sQS64o8AlC8PiZzEc9Rzl4y+3j06sLgEoOh+O+aXHYFZWiCquT43GVQ7LVrmilKVRVPIAEjWKQF4Uh7/9+M3xf9/xdk7py16RufrBK4EvyiiudMio3hJ1PT/VtssnnnGesA1KVLyYNUnrDpCcgPF6L9qos0spO6Aw1tbUEzRohePzMqxf345V5u8kt1irwWu0Kitq4vt6s5dMTirx8YV/OH5gWbDMEPsaol9n59FQKy618vuogbyzw/nM4Mr2RIcKCgJIYbeLvE9L5bctR9h8va9C9M99axi+3j8Ko91FU3tmvwZJ/azM0ig3FUQdLlmRn0aHYoSzPfT8NofiItu3+veH3CsfmZoMQPH5mZv9UZvZPrXPeZlf4Y3suVruCrrowp4yENpVsUxTsiorNroWSa6HodcPSlVrh6tqxdk1RtVxAX605HLC/9ZlZ24XgaaGEGXSkxOr4xxkZXDG8A3nFZo4VVVJQbsFstVNhtVNusfPqnzXOoXpZoltSQH6fC5qIJEncc0YGRworGix4EqNN6H2Zg2fg1drmoNRdpOnvD8Cq//pu3Mbyx8PQ52LQi3xloY4QPEFCr5M5s7c2dWuxKdz51QZ+33oMo14m3KAj3KAjwqhDJ0u0bxVBpEmPoqqaCFJUFMerXaktcmpeFVWlwur/pIdV3Dclg5vHdgnYeILgER9hZOhz81FV6JoURWp8OOVmO+VWGya9jNmmfe5sisrCXXkuBb8gNLlsaHt+cFE2pD4ent49cEkHazP8Vshep+XPAYhJ1ZaedAbN/6e8AMpP+N+OyiKwVQjB0wwQgicIKIrKS3/s4of1R5AliZPlFiod4sRiU7DYFIpq1dnak1davd+pdSQmvYxep80I6aSaMhKSBDpZwiBLSEhEmmBk53hkCYwGQ7Xfj3zKPVWvP2/0LlW7JGmJ5cIMOiS0JY8ebWKEj9BpQEGZhQFPz6s+3pNXWv35HN21NZN6JGO1Kfy5I5cXL+jLOX3bBstUQSMY0D6e3c9Mw2JXsNoUbv9yfZ0Q9FMpC1YOnrj2cL3js6gooNhqthfaNa7P6a9AxpmaYJJ1znW2nM45XmWRXLM5IQRPECi32vnvon2NuvfACdfTzXufnYbeRWbbhiQefO2S/o2ySXD6sHyf+1/MS/c4X7v32018vuogn18/lAij+KoJVZ6fvYN3luxv9P27jpUysINnx/amoN87B365wa9jAHB0Iwy+zv/jCIKC+BYKED9vzObOrzb6rf/L319FlMngFKElSxJ2mxVJkjAZDY7zEodPlrP6gBZe2ictlm7J0XRoFcF5A9MwOcKKjY7QYp0swosFNZzVpy0llTYe/GGLV+0VRQ18NW1BgxjauVWjBc8HVw9iQmayjy2qixLTyBmb+vjbV5DSB4wRYIh0LIWJz2pLRgieANElMaqJ90c6hZxXOTrjCDkvNdsorrA5+/MANptdq58lSZpvj6LV+api85EiNh/Ras+83MAipE/P6IlJr8Ool+nUOpK+7eKa9DcKmgf928d53XZ8ZhJ6Me0f0kzITK6TNPJQfjljXlro8d5rP1qLTpb47Y5RdPeQpLIpKEk9a2pk1abiJKz/RFvGmv9Uwzr98hLfGHcq6ZM1MaUTj9dQQ7wjAaJXaqzfM9HuP16K2aZUl6BQUZn++l9+G+/Rn7c5Hc+5azSZKaJmV0snMyXG5Wf5WFElm48Ukl1YwZO/bgfg1T/3ML13G7omi2it5sDXaw7xz++9m72rwq6oLNt7wq+Cxy3h8TDyTm1/0HXwrw6Bt+FU9s4Du0UInhBEvCMthO/XHeEf327y+zhXDOtAfpmZpOgwrHbNwdpiV+jRJkaEIJ/GKIrK8BfmV4vt2sRFiOiV5kL/9t6XxPnyhmH0bRdLuEEXGsve4XFa4sA/n/BP/3/7GkxRjgzQErTtD4Yw/4wl8AtC8IQgZpudf363mfk7a5JqVWVXrj52ZFyu4mS5lUDw6cqD1fs7nppKuNGHGVYFzZbjpWaXYgfghd938vJFfQNrkKBRdEuOJsKoo9yLYqB/e28lgzvG8+3NIwJgmQt+ug02fha48b682Pm48zitIrug2SAETwhy5GQFP3kZIh5MXpq7C4vdTqRRz12TugnxcxpzvMTs9tqYbiLbcnNhd25JvWInzCCTGG2iwqIlmrxxTBBzb/lD7BgioVUnrS6XqjpC0B0V1yXJOUR9cgN9hgRBRwieEKRLYhRbnjgDu6MGhaJq6+S1MytrxzVJBmtnWK7tuFxaWooKhEdEVt/vq0KmHyw7UL3fJjaMq0d2anKfguZJlY9acaWVPk/84XTtzq82MqOfSD7YHPCUMVlVYen9EwJkjQeqnJgVBaxlsGcefHdN0/rsdylM/3fTbROEJELwhCi+qn5eXKx9gdXOw7Pu0cku2/644Qh3f904P6Anft3Osn35vHP5wOBkXRWEBDFhBrJemE6nB2e5XeIShC6dE6PIemE6//xuM1+vrVuW5pNrhwTBKg/IMpiiodd52lbFE7EN66ftAJj6gm9tE4QUQvC0IMw2O28t2EtBuUWrtQWUllfyyxYfFdjzwLztuWQXVtCuVURAxhOEHocLynn5j11OYuf3O0cHzyCBV6iqytlv/sXW7OJ620WFhcgjQ1HgpS5QUeCb/kbcAWc845u+BCFLiHx6Bd6SX6r5SqhokTF2x/KWosDHK7J4/68D9XfQRO6YkE6ftDh0Mo7SFhJFFVaKK630axcnxM5pjKqqXPvRGqdSKACZKSJ6L9RRVOiQEOlW8Bj1Mn/dP56kmBCISirOgYpCLQePrxgksiufDgjBEwIcLzGzfN+J6vDOCqsds9WOpSrs27F9teawU9JAf9I1KUpLbuio2WXSydw9uRvDuyQEZHxBaHOyzMJZb/xF6ygjaa0iKCy3uKy59PcJ6aERsiyoF50s8dalA4gwbOLbdUfqXLfYFIY8N5/ubWK4f2oG4zOSgmAlkL0e3hvv+34L9mnOyoIWjRA8QeBQfjkXvbOC8ZmJGHUyH6846PkmL5jZry3jM5OqMzHLkoS5sgKdLBEVGeF0XidL6OSaIqI6WcKuqESa9KQnNS0rtKBloygqN366luzCCrILK9jkyNQ9pFMrVh8o4KEzMxmfkUSbuHCiTOIrpjlx1YiOrDpQgFEvE2HUVWdhr2LH0WKu+XANqx+eSFJ0EGZ7knvBwGvg4DJQ7JpQaQg3L4PEDK2quhDipx1+/TZavXo1GzduJC8vj169ejFz5szqa/v372f27NkUFRWRlpbGjBkziIuLA8BmszFr1iy2b9+OwWBg5MiRDB8+3Kt7mwNnvbGU4kobX66u6xToDbHhhpoq51T9v5VYti+f5fvyq2tpSWjLDJIEsiw7nZckCYtN8WrG6PwBaQzpFI9eljHoZQyyhEGnVWw36GR0skRppY1Km53JPZIx6UV4ektmc3YRa7LqLifsP15GWnw4P6zP5rnZOwGtVtuHVw8mIcoUaDMFjaBXaixL7h/P8RIzN3261m27k2XW4AgevRHOfrXm+Le7Ye0H3t//9kjtdeq/oNPoutXQTw09lxxlURQ7GCIg8vSc4W4pz3K/Cp7o6GjGjBnD3r17sdls1efLy8v55ptvOPvss8nIyGDBggV89913XH/99QAsWrSIgoIC7rrrLkpLS/n4449JTEwkPT3d473NgVUPTeKZWdvplhztCCnXhImqgk1RHdmL7VgcZSJUakLNwdEWnGpmade0NrXP55wsY/n+pq11f7/+CN+vrzvN7YpLh7bnuXN7N2k8QWjTr10c398ynIP55bw+fw+jurbGoJOptCocKihzWtrafKSI0S8uZOsTU0T0XjOhzGzjr73HWX+o0OX1ly/sS0ao+GWNexCG3QZ2M9jM3i93zfln48a7Yz0kBDH3UJBoKc9yvwqe7t27A5CTk0NxcY0z3I4dO0hMTKRnz54AjBs3jpdeeokTJ07QunVrNm3axMyZMwkPDyc8PJwBAwawceNG0tPTPd7bHAg36ng2QKJg7qaDTRY89dE7NZYt2dq09zl923LP5G5+G0sQOgzs0IqBHVpx3oA0p/Nbs4s46w3n+m1DOrUSYqeZ8OXqQzz4Q/21tPS6EHkv98yDzy/w/zj6cIhoBd2mQFwI1OoKAi3lWR6UBfa8vDySk5Orj41GI/Hx8eTl5REZGUlpaanT9ZSUFHbt2uXx3uYiePxJUYWVvk/+4bmhD3j4zO7cMKZzQMYShD7vLN7H87/vrHP+nL5tg2CNoDEM6+x5yebOrzYiSVLw3tcNn8PPtwZuvLu3QqR4triiuT3LgyJ4LBYLkZGRTufCwsKwWCxYLJbq4ypMJhNms9njvacLqiMU3ebY7HYVq6JgV1RKKv1TUyszJZovbhhGq0hRCFLgmjCDa9+ts/oIwdNc6NQ6kkfP6sHTv22vt93fv9zAmb1S0OvkAFlWi5QALpkPuq7Zip1169axbt06QFt68gfN7VkeFMFjNBqr/+gqzGYzRqMRo9FYfazX66v3TSaTx3tbOseKKhn2/PygjL3zWAk7jxYzIr15/ucX+J+rRnTk4sHtyHx0jtP5Spsdoz4ID0ZBo7hkcDuXgmdarxTO6tMWo16mZ9uY4IgdgDZ9tLISJbna8dKXYfU7je9vxN+hw0it8rnOCLIBdHotkiuph29sDgIDBw5k4MCBALz77rt+GaO5PcuD8olNSkoiNze3+thisVBQUEBSUhLh4eFERUVx7Nix6uu5ubkkJiZ6vLelExdhYHxGYp3zsqTVwDHqZcIMjXtLdbJEu1bhpMWHu23zwA9bqhMfCgSucDXL882axkUjCoJDz8fnujz/+9ZjTO2VwuQeybSNc/89ERBWvwcvd9O2pogdgOWva5XQP5kBn54HO36BHb/C3j/BfvqsHDSG5vYs96vgURQFm82Goiioqlq9n5mZSV5eHtu3b8dms7F48WKSk5Or1+369u3L0qVLqaio4MSJE6xfv55+/foBeLy3pZJfaubaj9Zw+GQF6UlR1VuXxEg6tY6kfasI2sSG0TrKRGpcOG1iGxYyaldUDhdUcOSk+zD1QwXlHK7nuuD0RFVV3lm8j8xHf6fjA7PqXP9+fXYQrBI0lkemdycxOsTTCGScCUk9ITIRolIg2kfLprYKTQAtfRn+fAIWPuebfps5LeVZ7tclrSVLlrB48eLq482bNzN27FjGjRvHRRddxOzZs/nxxx9JTU3lggtqvO3HjRvHrFmzePXVV6tj99PT0wGIjIys997mxOwtR/n33F0oqsqZvdsA8H+LahJpdW4dWR26/sf2XHfdNInubWIY2qkV4UYdEQYd4UYd5RY7nVpHMrxLAgZZxmSQ3fpnCE4/lu09wS2fraNLUhTtW0Vw4ERZnQR1ALePT6drchRnCx+eZsX1oztjtav8a05dB/Q+T8ylzGKnd2osv94xKgjWOYhNhVuXO597tg1YvfBVMcXC2PvBGKEtX6kKKFaw2xyvVlBsgATDA+gcHcK0lGe5pKqipjFoa5w33nhj9XFV6F3tKuO+5D/zdvPa/D1+6bshtIkNq04eKEtazp8YR6X2qnw/iqrV6lKpyRfUOtrI65f0D2hCOX+/J77i1M9SKODLfztXszin0rl1JJcObc91ozp5LC0Ryu9rsG1rymepobb/sP4I7yzej07WEpRuy6m/kOjS+8cHpHaeV3+HuRSeT238IOe+C10ng86gJRiUff8DL5ifpVD8TgoGIu97kBifmeRS8LSOMlULj6pkgtWJCR3nqvY5JfGg6kg8aLYpXttxtKiyUfbvyoU/d+Ry8eD2jbpf0HyZd/cY/vbeSk6Uuvdv2H+ijGdm7eCZWTt44bzeXDJEfE5CnXu+2eR12wemZdbr7xcQzCXwcnewlDS9rx/rEQN9LtYSHIpaW80eIXiCRL92cWS9MN0vfc/fkct1H7tPC99YZvZrS/uESGRJC1Of2quNz8cQhD5dk6NZ+8hkl9fKLTYe/WmbU2buB37YIgRPM2DLE2dwML+caz5aw/GS+oMTxnZLDG5RWMUOlnLfiB1PbP4ajFFw1iv+H0vgV4TgaYFM7J5M1gvTOXCijCvfX0mlVUGFen+Re8NPG3Ocjj+/fijJMSY6JkQGL0RVEDS+WXuY+7/bHGwzBE2k84OzUBro2DDttaVOx1N7pvD2FQN9aFU97PgVvr48MGNV0e+ywI4n8AtC8LRgvl17mMMnG7dk5Q2X/W8VAJN7JPPelYP8No4gNGmI+9+Rk+Wkxfvf30PQcG4dl86bC/c2qQ+L3ftl9CaTGiBhVZv/TXA+vn4BpAXBDkGTEIKnBXPflAzO6dkKgKioKN6Yv5ev1/omJ0p0mL46dPWOCek+6VPQvLh4cHsnH676ZnzeXbKfp2b0CpRpggZw75QM7p2SwbtL9lVXuXfH1SM68sQ5PQNkmRti2sK1f8AHZwTPBp14dDZHxLvWQikos1BQZsauqHy+Jocv1uZ4vqkBfHnDMHqlxvq0T0Hz5qJB7bhoUDtWHyjgondWOF37ZMVB+reP49z+aW7uFgSbG8d04bwBaYx4foHbGZugi50q2g+FB7Ph5QywlPpnjCfqploQNG+E4GnmlFtsrNpfwDUfrQnIeNeO7MRjZzffdOsC/3GsqJIDJ8r423srXV7v3y4+wBYJGsJF76xg9YECl9cyU6L55NohAbbIA6/18ZHYkSAsFsJitHISkgzDb/dBv4JQQwieZsxLc3fy1sJ9nhuewuXD2jOyS2tqgiy0vBsSIEvaftXroI6tiDKJj4mgfj5beZBHftrqdO7lC/syo19b4dDeTOjfPs6t4OmWHE1STMOyt/sVVYXeF8Gq/7pvE5MG5/8P9EbQmUBv0gSNrNdy7UR6rgwvaFmIJ1kzZl9eWaPu+2zlIbokRqGXJWRZ0l4lCb1Oe9XVOrf6QD4SEhVWO8UVVsotdv42pD3hRpF5uSWjqip2RcXueLUpKnZ7zbHVrpBbbAZUKq0Kn608WKePZ2Ztp09aLF2TowP/Bwi85niJmZlvLSO70HXZmMRoE+cOSKXSaseklwMXjl6UDR9OJabwUOPuH3QNlB3XkghKOu1V1tWIngJ9zX7Vpqu1r6oQ3QZkIdhbCkLwNGPevmIg3649zH2NCA1+8te61ZC9JSu/TDigtlA+WZHFYz9v80lfJ8utTP7PEubcNZrMlNDLoizQOJhf5lbsgCaIrvmwZsl8/j/G0iUxyv+GHVoBjRU7AAuebroNw26Fqc83vR9BSCAETzPnwkHtuHBQO7fXi4uLUVSVyKhoFuzM46ZP1zVpvNFdW3PflIwm9SEITQ6cKOPVP31f7kQXzAR1Ao8M6tiKv0/syutelLoZ1CGelEAtbfW+AHrMpLi4EBQ7MVGRoNq12lc5G+DTc/0zrj4MDOFassFB1/lnDEFQEILnNECWJAw6mSk9U8h6YTqKY6nCZlexKQq7jpVwwdsrPPZz2/gu3DclMwAWC4LBGwv2UFDWtOSUVcRFGJhz5xhSYkPI70Pglnsmd+OOCemUVto4642/XM74+CszfL3o9KAzgg4w1ZpV6jKhJorKbtOKhlpKtermuVuh+zna8tXxXbDx84aNaavUIsBE6HmLQ7yjzZRD+eWYbXaqUr9V1dKy2at8LhRsdpWiklLsKpjCKrDaa/wv7IrW9tnZOyiqsHocb/F94+iQEOnfP0oQcI4VVTLs+fk+77ew3Frd7+qHJ5IULYRPqGPQyRRWWOtd3gopjm2F7HXarI9i184l94LW3bSq59YKzQeny0TY18DP+J+Pw5RnfW+zIKgIwdMM+XHDEe7+2vtCf76goMwiBE8LJNygI8qkp9Rs89sYQ56dz0NnZnLjmC5+G0PQOOyKykfLs9h/vJSiCiu/bT7qsl0wl7F1OesxrnodwiK1auZIWuj4lm/8N2j74f7rWxA0hOBphoztlsTI9ASOFVUiSRJVHhJVrhKSI8wcAFVhZ27jorlqc+7/LXc6/vm2kfRtF9fkfgXBJTbCwNYnp/ikr1s+W8fvW4+5vJbvo6UygW+ZteUoT//mOYDhiuEdAmCNa8J/uR65/Lj/BojvCNfNg6gk/40hCAmE4GlG7DhazGX/W1Vdw6gqPFQC6vqFaqJHVfxT4+ZoUSV93ftKC1oAR4sq+HNHnvNJVXVeRnV8FlUgMyXGpeDJTInmwWnd/WuswCtyiyu54v1VHC6owKYoWO3e1UO78v3VXDa0PaoKiqqiOF4jjDpm9EtFJ/vYMV1R4Le7YP3H+D0o/GQW/HQLdBjhOFH9a9Fhix3SJ0HqAH9bIvAzQvA0I75bd8RnTqVNoXubGPq3jwu2GQI/M/alRVhsTRfMI7q09oE1Al/w154T7M5teHbijYcL2Xi40OW1/FILN4zp3ETLTsFuhvUf+7bP+tj7p7a5Y+GzotREC0AInmbEI9O7071NDPd+G1j/nVPZcbSYY0WVJIdS5lWBz/n5tpEuEwpqWbmlWvsaJWYbP6zPrtP+g2UHuHls59DK1Huacv7ANCb3TAbQojVrJZfMPlnhVbTmRYPSHJnYJWLC9f5Z7jKEVwuM4qIiQHWEpSvatvQVWPKi78etok0/6DxO25f1kBmECDWBzxGCpxkhSRJbs4P/K+PWcV2Ytz2XIycrmN6nTbDNEfiJ7m1iePbc3l6335tX4lLwXDQojcMny0mMNgUuS6/ALTFhhjrnDheUexQ7M/u15clzehEbUfd+vyJJgASVRfBKD232x5fEtoeJj2qO0PsXag7L/S/37RiCkEAInmbGE+f0rFOxWKn1K01RVQ6cKGP663/5zYb/W1RTv6t36njaJ0T4bSxBcNmeU8yZry9tUh/frD3CN2uP8Oy5vbhsaPCcXwXuOVpU6bHNTxtz+GljjtO5m8Z2Dpx/lrnE92IHoOgQ/HBDzfGGz+Dn21y3veBD6HWe720QBAQheJoxr/yxi9cX7PVL310SI6sjwMot9jq5Oe6YkM6gjq2E2Gnh6HVNn5HJTIkmIyWas/q09YFFAn/wf4sa9z1SXOG/dAZ1yG5alnifEC8Ee3NGCJ5mTGyE0ed93jSmMxcOakd6UgBq5QhCnm7J0V5l2F1/6CTnnZK6oIrZfx+N7OsoHoFPefeKQbw2fzdHCyv5YUPdZUmtzUDO6JkSYMtq0W4oRCZqBUH9yf0HIKKVf8cQBAUheJox143qxKTuSTw7awd/bM/1SZ/vLNnPO0v2Vx/HhOm5ekRHFBWsisIVwzqQFi9mdQTODGgfz9pHJjHombqRLrkllbSJDQ+CVQJvMepl7puSycr9+S4Fz8WD2gVX7ADEtYP73MxEFefAK01YWotKhslPQd9LGt+HIOQRgieEMNvsvPD7TvYfb3iiwLHdErHaFQrLrcgymK0Ke/IaHn56KsWVNqdls8W7jjPnrjFN7lfQspi77RjvLN7H4I7x5BRWOi2BfrnqEPecIQrONgc6usmm/vXaw1w3uhPdkqMDbJEH7DYoPwF5npMn1ktpLvx4E2Se5VyzS9CiEIInhFi5v4APl2UF2wxA87uw2BX6pcXRISESWQKdTuKCgWnBNk0QYiiKyk2fuvevuFQ4KjcLdh4rZuqr7h3Ut+cUExduICbcgEkvh0bE3QdnNN63J22wFpllt0JsGrTtD4dWaOUrZIOjaKnjVW/Sqqjrw2r2RXHRZod4x0KIsd0S+em2kZwst1Qn+XSLi++aH9dn88umnDrnDTqJM3qmYLbaqbQqVFjtVFjsbD9a7Lb7ncdKAOrMNq0/WIgswR/bc/nznjGkJ4XYLz5BwJFlidUPTeRgQTlWu0JppY0bawmg9YdOcmZvkb4g1Ckz2+u9ftfXG+ucS40Lp1PrSPLLLDx8ZndGdQ1wksnxD8GPtzjy89jBZgGr4zurywSwlIG5VKukbikFSznYHLOPR9bU9JOzHnb84ju7YtK0au2yvnqLREINT4AL3oPoZN+NJfAaIXhCjH5NrE/VKzXGqZaWxWxGkiA8LAxZdqSLkyS+XXu4Uf3/uaPGV2jSK0u8cmgVtHySYsJIigljwc5cNh9xzhW17qAQPKGMXVFZk1XAO4u1dBN3TepKmEGHxaaw4dBJFu5y7yScXVhRvXx5+furAvN9YLfC7/+Ete9D/yu0pICyTputQcLjr8XV7/rfxuIjdU7pqna2fg/Db/W/DYI6CMHTQth/vJRrPlzjuWETeXpGT5AkFu7M48UL+vh9PEHz4YL/LmftwZN1zg/tJCJeQpnnZu/g/b8OVB+/+ucej/fcNyWDtPhwwgw6THqZSqvCwA7x/jSzhk1famIHYMOn/h9v3EPakpck19ocFdurhFbtTdbXWhYzaMeSTGlpMcgGojoP8b/NApcIwdNMKDPbeHb2DvKKKzHbFKx2BZtdxWpXsDhe/cFVwzvwjykZGHWy07r9FcOEX4ZA48CJMm78ZK1bJ/l3luwPfoSPwC3Xj+7ErmMl/LX3hFftv75xGEM7J/jZqnrod7lWufbYFkjuqS1nlZ2ARc/5pv8rf64pK+FDlGKHC0Eo+D6dpgjB00yY9tpSDhWUB3zc7UeLkYAwg85jW8HpyaJdeW7FznkDUnn+PO/LUwgCT5vYcD67fmj1cYXFzr//2OU06zOscys+vW4oBp3fa5d7RpZh4FXO5+Y95pu+r18ArTppZSxq+d9Uz+oImjVC8DQT/u+yAZz1RsPLRfRJjWZzdkmjx12TdZLeT/xRfXzdqE5szS5i1YEC0uLDkSWJ964cREaKcF4+XblmZCcijXru/34zAAmRRvLLLAD8sD6bx8/qiUkvBHNz4fUFe5zEDmgRpF0f/t1l+16pMfx6+6jgRm2NfwSSe4Ni06Kovrumcf38b4LvbDJGgTFSezVFoR94C7ZuwucxmAjB00zolRrr0SHwrz0nuPz9VU7nmiJ2XFH7i/DISc1Z8aPlWeJX/GnORYPbcdHgduw8VszZpwjzDYdPMqhjK6JM4usmlMkvNbNgZx7/rVUrzxu2ZhejqOCDKiSeUVX47HzYNz8AgzWRqsgwtECPiN9upviexgWLCHyD+AZqZmSdKOOMV5dgsfnHZ8cVY7olYtLL6GUJWZbYnlPMgRNlJEab+O9lAxjQPkDOioKgsCargOyTNYkEq37IV9VaqzonIbFi/wmsducomasdzvQ/3TayyVGIgqajqipvLNjL12sOY1dUbIrKiVLvinLO6NcWo2NZKybcQI82MQzsEI/On6VDbJVE/Hw9HFzsvzGq0IdDmz6AVFOlvfZyljESBl2r5eY51YG5KkpMkrUcPXKVw7IEljKK9UH0exIAQvA0Ow4WlAdU7IC2RDEuIxFFVbErWr4gRVFRVOjZNlbUSWrBbDlSxIVvr2hyP5O6J5ERall6T1MsdoVX5u1u1L0/n1It/ZHp3enY2nV2Zl8hleWhD4TYAS1Hz+FV9bdp2x/GPdDwvovd5z0TBAYheJoZY7slkvXCdArLLdz+xQYOnyxHAiw2BauiYrMrWGwKZZaaJGLjuyUQFxlWHdllUxT+3JHn9Zg/bsjmRzcFBTccOslLF/Zt6p8lCFF6to3hgWmZ7DhajOqYuKmav1EcJ8rMNhbVk6sF4J0rBvl3FkDgNSa9js1PnEFRuRWdLPG/pQf4YNmBeu+JizAwuGNNegGjXiYh0sjfhrT3t7mose0pvnM/MdFR2pKWqmhb4UF4e7SWcLAxDLvNc5tT/ZIMETDyzsaNJwg6QvA0U1bsy/c6jHTh7nyfj5+eFMXgjvHcK2oktWhkWeLmsV3qbbM9p7hewfPYWT2E2AkxYsIMxIQZAHjs7B48elZ3Vh8o4OJ3V7psP+/usSRGmwJpojM6g+aMXJvWGfDAIfj2atg7r+F9rnyrZv/yHyB9YpNMFIQ+QvA0U6b1bsPyByZgtinsOlbMzZ+tD+j4+46X0jctjmdm7eDlC/uKZa3TmB5tY9j1zFSe/m07n608VOf62IzEIFglaAjPzd7Be0vdz/JMe20pfdNiuWlsF4YEO5GkYoePz4aDy3zX52fnQcfRDj8cYPKT2tKVoEURAkkVBI2lraOOjasaN/5GVeH79Uf4cUM2m44UBnx8QWhh0ut4ZmZvsl6YzntXDnK6NvHlAPlfCBpFqdlWr9gBOFFqZv7OPC7/nwf/lkDwVCvfip0qspbCgcXa9utdvu9fEHTEDI8PKK60MnvzUR74YQsAk7onEx9hwK6qqKpWq0ZRVYfTr+bsqzjO2VUtaqKqjao6fCRUUKk5VlXtPu2a6jintam0+seJOdyg4+7JXWvZUGMTaAnKwo06+osordOG1+fv4ZV5uxnvmLVZuOs40SY9HVpHUGlVqLTaqbQ6+1R0bxMTDFMFXtLr8bke2+hliXCjji6JUdz4yVoUVSUpJoz7zsggPtIYACtrMfmppicajG0HYXFQng+2Sqo900b/A5J6QLuh9d0taKYIweMD+tRKzAfOBTabA7IEiot6e6senli9zi84vflpQ7bTTGLtgpIlZhtbs11HoEzpmczz54maa6HMUzN68tjP2+ptY1NUSiptbDxc6HT+i1XOS5h7n52G3t/ZmEfeCX0v1ZILZi1tXB9Fh7XtVP54xPm4/xUw483GjSEIOYTg8QEz+7Xlp1rhmqO7tsagk51mZdTq2RvttXo259RzOM7V2q9qWxUV49y2ZhZIqTWG4pj0sSsKiqoiSRKKCsdL6ubbcCV2QBNy/3fZAHqnxtKuVYQv/8kEzYzG/oqfuy2Xudvmsf2pKUQYxddNKNIrNdZjmwHt48grMVcnG3XF0E6tkAOVbTkqEa7+reY4bwf83zDfj9NGRKC2JMQ3kA949ZL+vHpJ8BzcVFVlxb58Km12JCSKKqw+8+u59XPNGXrdI5NIiApilIYgqFSlQ7DaFX5cn02ZxQaARE2YOkBhuZXX5jtX227fKoJwUYstZPl0xcF6r69+aCJJMWEBssY1hq1fwx/3+qazxEwYerMW+SXptEKhMW1807cgpBGCpwXwwbIsnv5tu1/HGPfvRQC8dkk/JmQm+3UsQehi0MlcNLid2+t9n/yjzrlRXVsHt86SoF7c5diq4soPVvPjrSMJNwZPtIb7SuwAHN8Jv90FvS6AC973Xb+CkEcInhBn85FCznnTDxEJDaSkUvtF/8aCvULwCFySU1hBUYW1znnF3ZqpICTIemE6R06WM+pfC11e33mshBKzNaiCp/jWrcTYC2pOzPoHHFnTtE73NCJ3j6BZIwRPE/lm7WGem72DML0Oo17GqJc5UWqmV9tYUmLDCDPImPS66qn/2v44Va+KWuOL43SM519f/sSgk/jg6sFkpESjkyR0siScmAVO7DhazAPfb2bTkSK3bfxdekDQdJRTAj27t4nh42sG0yrS6H8nZHeoKsx5gJhVbzfsvvB4uOADSBui1bKS9SDr6mZNFpx2CMHTRB77easjLNz5l623WZBDkXvP6MZPG3PYm1fKv+bsxKCTsSta6Py9UzIYn5EUbBMFIcK01+pGyYzokkCHhAi2Hy2hsNzCZUP9X35A0DSMemdRs+NoMUOem0/WC9ODZBGa4Gmo2IluAyVH4dNzIa4DGKNgzD+g1/n+sVHQrBCCp4lse3IqRRVWzDY7ZqtCpc3OV6sPkxYfjsmgo8Jio8KiUG618c7i/dX39W0Xh+IQEVrFYgVFBZuiYLer2FW1upKxXVFRau3b/LxE8O8/agoLnhpu/PcvNrDlySl+HV8QmszbnssNn6z12G75vnxS48KZ0bctV4/oKLJwhyiVVjt7ckspqbSSX2Zx2eZ4iTlwJSUUBazlWl4ca4W23biI8rws5MKDhJXnwOp36u+j5GjNfqHDGfu7a7WtNjcvg5RevrVfEPIIwdNEdLJEq0gjT/66jQ+XZXl936ZT8lmEApkp0WSmRKOTZX7dnIMswVPn9KJLUiQ6WUYnSXROFMsTpyv2U9c96uHbdUcA6JAQwcTuwucrFLnx03Us2V1/0VezrZGFORvD+5Mge12d035JiKH6J1mrILQRgsdH/LGteSUbrM1z5/bm4sHtnAo8vnyRyD8hcGZqrzbVSxw5hRWMeGFBnTatIo3cN0UrKBsTZmBCplj+DFXumtQVs9XOqgMFbtukxQcw/9aoe7RkgordIUi8nMmWDTD4epj0hFZgVPjqCNwgBI+PWPbABLYcKeLzVQcd/98kFEUl3KhjyZ7j7D9eVu/9kUYdZZYA/pqqxZGT5aKataBBVFhdf1YLyiw86Cix8urF/UQ4eggzoH08d0/uxiVuKqRfMrgdiqIGbkmy+1nwaK0Zpyc8J0QEQLHCqv9q2/iHYehNEOblvYLTCiF4fEjvtFheSPMujX6p2cb6gyersyBLaI6C3607wp68Uv8aWovPrx/KiC4JARtP0DLokhhVPdtzssxC/6frhvje9fVGhnZuRZvY8ECbJ/CSofVUPv9qzWEUVeXFC4I02/voCbBbKDmRQ/S7gzy3B1j4LCx7DR4KXnSrIHQRgidIXPvRGlbXM5XsL87p25ZOrSO5fFiHwDkjClo09c0AvPLHbl66UCyPhiqSJLHo3nHViUVP5Zu1R7h2VCcyUwJUAFaxw6IXYM8fIMmgKoTTwPw/lsD9YBQ0L4TgCRKPTu/Bv//YRYXVHlDho5Ml7p7cLWDjCVo+MWF6njynJ4//4lyAckD7OB47u0eQrBJ4wq6odHlotsd2ceEBrIZedhyWvOh0qsEPqc7jfWaOoGUhBE+QuOXzdfUW4msKD52ZycTuyaiqSnFJKYoK4RERqKoWiSUQ+JKiCmsdsQPw2iX9iRaJKkOWqmLErnj0rB5cN6pTAK1xEJ0Cd2+H0mNaHh67BT6cVv89U56D4bcFxj5Bs0YIniCQXVjhN7GTnhTF2G5JRBh1WnZkuwFZkoiLiUCWwWJXsCkqsiNzsiwhHEsFTeJoUSXtW0VwqKDc6bzFrnC4oJy0+HDxGQsx7IpK14d/d3v96d+28+OGI1w6pAOXBjpxZGyqtnnL3Ie0La4DnPE0dBztyKwsa8VBJVnbqs+Jz+LpihA8QSA+wkCPNjFsP1rsuXED2ZtXypRXl/i0z6X3j6ddqwCGpwqaFa6yLQNMfHkxALePT+deR6i6IDSQJbhqeAc+rqdS+tbsYh76cQsP/bjF5fVdz0zFpPd/fa3Sa5YQ9eEYzw0LD8I3VzZ8gMcLhQg6TRCCx0+oqsq+42VYbAqyDLKkzabgqKr12iX9tJpZaNmTV+zL55lZO4JsdV06t44kNkIsSwhcU+kmPL02by7cy6+bc7hxTGcuG9ohAFYJvGFm/1Rm9E/lwPEy/vHtpgbdOy4jEb3spxpbJ/ZAeb6WX8cQBtYySv/2C1HhYVoI+q7fYcWbvhvvybia/Ys+gR4zfNe3IKQQgsdPfLn6sNtfRqHOB1cPEhXRBV5h0nv30DuYX87DP24VgidEeHPBXl6et9tzw1O4f2oGt45L94NFDrLXw3vOTsdR/hutLnar5zaCZosQPH5iep82/LH9GMeKKmtVQdcqoNc+PlzgH18ed3x+/VAyU6K1GSdZ8+PRSRKyDHpZFgkIBV7T8YFZXrX77LqhtIkLIzkmzM8WCbzlkiHtWXPwpMfSEqfy4pxdvDhnFwAj0xP49Nqhvk1MWBGgiNXweK2wqKpoX8io2v6yVyGlDySKSNaWiBA8fiI23MBH1wypPv505UEe/WlrEC2CwR3j6dsujiiTeNsF/mXuXWPolhwlnJVDlMRoE59cO4Rdx0q45fN1HjPBu2LZ3nwqbXYijD78Pmk3DAZcBfl7QdaDzoBVAcluRq9YwFwKKb1Bp4f9i6HocOPGqTipbadSmgs7f4XEfzTt7xCEJOLJFyDaB8DpNznGREpsODpJy7ejkyVURUEnwfIDhazJOkmvx+dy45jOxEcYMegk9LKEXic79mX0OgmDTkYvSxj0MgPaxxMbLnx4BHXJemE693y9kR821M1qW+U4X5WNWRCaZKRE8/n1Qxn+fN26aPVx0aA0zh+Q5luxA2CKgnNedzpl3r+GqE8m1ZzIq5sCoUF0maD5B8l6TTiNugciErRjWQeRiU3rXxCyCMETIMZ2S6z3y19RVGyKygVvL2fzkaJGjZFbbCa32Oyx3btL9nvdZ2y4gU2Pn9EoewQtn+fO6815A9K4/P1VLq9vOHSS/u3jA2yVoCGkxITx3Lm9Pfoc/n1COvecEYRoO7vFt/3tO0XchcXCOW/4dgxBSCIETwBQFJVl+06gkyViHInYFFWt5cujRXUpKo0WO/6iqMJKxwdmkRhtYs3DkzzfIDitCDPoiA5z/zWy6XChEDwhTGG5hX5P1a2DVkVqXDhjMxKRgOtGdQ6cYVVYK5DMJQ2/78x/a0LJZnY4IrtJsqgzwJCbmmSioPkgBI8fOXCijPFuatQ0N+LEspbABUt2H+fKD1a7va64T+YrCAH+b9G+eq9nF1bwxapDrH1kUuDTU/z5JPz1CpGNuXf2vdD7Ijj3HfBX+Lyg2SEEjx8x2zznKAlV7pncjb9P7BpsMwQhjuzBKfnPHblcG4wSBQKvGN21tVdL3IOe+ZOebWOqZ6MtNoX9J8rokhjJ7DtH+ycBYfok+OuVxt+/5RvY+r3mm+MLVEXLA9TnYpjxf5r/j6BZId4xP5KZEuPRadPb0N5A88q83QzrnEBmm+jqZTiBoDaHC8rd+u5UsXxfPocLykWm7hBleOcEr9tuy6mbGX7f8TLWHTzJiC6tfWeUqmpV09v290FfdrD7+Ifn5q9h+sugE3UJmxtC8ASYCoudk+UWKq12iittwTanXi56ZwUA/3fZAM7s3SbI1giaK0LshA57ckuY/J+GlZ55ZHp30pOiMOpkjHoZg07bCsstxEUY6dE2puGGvNIDiutG9/mEwTdAj3McUVf6mvpZVTW13G5etDFGgT6A1eMFPkUIngDT/bE5wTbBLUZH1lwJ7f++TpLolBjJ4I6tgmuYICRp1yqCrBemc/E7K1h1wHXCuIbMIAj8j17XcH+WP7bnsnJ/PqAtYT49s1fTk0hGJDRd8HQYqVVXr1qysltBsWn9zn0Y9CbtWLFpM0Z2q7YkFRYHl3wOsWlNG1/Q7BCCJ8A8e24vXvtzT3W1coAyi42UmDD2n9Bqb2nVpUFRNKfBQPHbHaPoliymaQUN4+ubhvPb5hxu/2KD03mDTuLh6d2DZJXAFZ1aR5L1wnQqLHau+3gNy/fle7xn9SliNiHKxPPn9W6aITe7LjhL9jqwVmq/uP76D+z5AwAlMgm5w3DY/nNN24PLGj/+7jkw+PrG3y9olgjBE2AuG9qhQfWEKix2bvhkLauzCrDYFD9aBmecMtU9/x9j6ZIY0Eo2gmbKWX3a1hE8VrvKWW/8xdL7x4tlrRAj3KjjixuGATDx5UXsa0Cm5a/XHOKyoe3plRrre8NSB9bstxsKT2mzy3JZnrPYaSqz/qFtAOe+C30v9l3fgpBFxOuFKD9uOMJdX23ggR820yrSyJAgLCttd+GkKBA0lLT48GCbIKiHUekNczhWVPhj2zE/WVMbCdoP9/8wO3/1/xiCkEDM8IQYiqLy9pJ91QX6Asklg9vxwvl9Aj6uoPlTYXEdCdO5daSopxXCzNl6lI9XHHR5bXTX1jw4rTthBhmTQUeYvua1Mb5ADUaStCWuhmCIhGtmQURrLalgVbmIKgdmWQ+STuTmOU0RgifE+HVzTsDEzssX9iU+0uBUQ2vdwQLMVoUKq53+7eNpFSkiEgTuKTXbqLTaKaqwurz+7c0B+IUuaBQfLTvAE79ud3u9a1I0368/woTMJNZkFTCjXyqRqkqlVUICbIpKfISxOtjB5yx4xvuyEsNvhzZ9NUfl4hwoOaYJG0nWxE3tSCtqCfA6YtyFOI9oBYlBKKkh8DlC8IQQX685xD+/r7+ejS/5x7eb6r0ebtCx4+mpAbJG0Nw4XFDO6BcX1tsm0iS+YkKVEg9pMT5YdgCA9//SXl/9c0+dNm1jw1j+4ETfGXVsK7w9suH3rXjTdza44qrfoNNo/44h8Dt+/TZavXo1GzduJC8vj169ejFz5kwAjhw5wsKFC8nJyUGWZTp27MjUqVOJjtYihBYtWsTSpUvR6Wqyd95yyy3Ex2s1eY4dO8Yvv/zC8ePHSUxM5JxzziElJcWff0qjmbvtGHO3HkOWJb5bd6TO9SiTHqtdwWpXvErDP6l7MpIEsqSFiEoSSJKk7aOdB7CrYFcUzBYrigqSrMOuqCiqil1RnffVmuKlRr1MuEEmzKDjjgnpvv3HELQo2sSGcdGgNOZtz+Vked0ZnsyUaMIMfsjAK/AJd0zsysTuyZz5et2Iqd6pseSVVJJbbMaok7HYtYCJ6DA9OlmqjjJ9/JyevjVqzf/cX+swCmLa1MzcqIo2A+S0nfI5VB1fqpJUs4+KNpOjum5bfd7RJjET0gY35a9q9rSUZ7lfBU90dDRjxoxh79692Gw1vyYqKioYMGAAF110EbIsM3v2bH7++Wcuv/zy6jY9e/bkvPPOq9On3W7nq6++YujQoQwePJh169bx1Vdfcccddzj9owab/cdLmfDyYo/tSs01/y4dEiLIKzYTFaZHX+tLRSdLdEuO4rpRndHrJHSO85KEdl2SkKvaSxKyTPWXUnlpKbIsERNdE24eFabHEIg1eEGLRq+TefGCvhwrqmTY8/Odrk3tmULH1pG8t2Q/V43o6L9lD0GT2J3rujDn5zcMDU6G9bP+A9t/goqTda/1v1zLnRObBq1EuZJA0lKe5X4VPN27azk4cnJyKC6uifjp2tW5RtOQIUP46KOPvOozKysLRVEYNmwYkiQxdOhQli9fzoEDB0hPD50ZiagwvfOPilrEhhsIM8iYbUq1vwzAwfxygOrj2hw4Ucbcbbl+tXnrk1OIEksQggaSFG3ivikZvDS3xvdsTq0onphwPRcPbh8M0wQe2HykyOX5Pk/8wfQ+bZjeuw3d28TQqXWjSng2HEmCYbfBwmfqXvvpZufjxEzQh8HRjdrxpCch40wtE7Ikg84E0cl+N/l0oKU8y0Pi6Xbw4EESExOdzu3evZt//etfREdHM3jwYAYP1qYU8/LySE5Odor8SE5OJi8vL6QET1J0GAeer7+OVhVmm53cIjNmmx2bUrPkZK+1/FS1BGVTVGx2FatdYcHOPJfLZI3l27WHuWak+OUkaBiyLHHb+HRuG5+O1a7Q9eHfna6nJ4lcTqHKo2d1Jy0+nKd+q+u8PGvzUWZtPgrA3menBSYyC2DknTD0Ji1DcsEBygqykSpOErHxfThay+/w+E7n+/58XNtqc8mXkHmm/20WAKH/LA+64MnNzWXx4sVccskl1ed69uzJwIEDiYyMJDs7m2+++YawsDB69+6NxWLBZDI59REWFobF4qU3f5CwKyqr9udz6f+0YothDj+ZDgmR9GobU+1PsybrJAdOeJ8EzFfcM7kbVw7vGPBxBS2DvJJKHvx+C/N35tW51q9dfBAsEnjiZJmF/k/P89iuf/s4/4qdA0vg47PdXm7U3FJse0joDO2HNdqs5s66detYt04L6y8vL/f7eM3hWR5UwVNQUMDnn3/O1KlT6dChJvtwbYXYrl07hg4dyo4dO+jduzdGoxGz2ezUj9lsxmgMjfDpIyfLue3z9RRX2uoVLpVWhUqrQmF5IZsOF5IYbUInSeSXmd3eU8U9k7tpTsu1/Haq/HmqHJnLzHYUVaWiohK7qqI31Pz7qCqoDse8Sd2T6d9ePJAEjeebNYe5//vNbq93eWg2Kx6cQJtYkYAwVDiUX86Yl+qPsAO4flQn7p+a6WdjVnlsYul7FcbOI2py6dTOsYPDd0C1azWzOgyHcPGdNnDgQAYO1DJXv/vuu34dq7k8y4MmeAoLC/nkk08YM2YMffv29dhedTjDJCUlsWLFClRVrZ4Ky83NrZ4mCzZzth5jk5t18fo4XuJe6Kx7ZBIJUSa31+ujar01JqYRFY0FAi9Yvu9EvdfP7J0i8jmFGCmxYSRGm+r93gG4cWxn/zucj77Htc9OLYybPoZzX/evHYJG0Zye5X4VPIqiVG+qqmKz2ZBlmdLSUj755BOGDBnCoEGD6ty3c+dOOnToQFhYGDk5OaxevZoJEyYA0LFjR2RZZtWqVQwaNIj169cD0KlTaPieXDeqE1N6pmC1K9gVFbNN4XiJmWs+WtPoPie9spi4CGPdlFgSxEcY0dWTydZqs5GeGMHT5/UTIcICv/Cfi/txRs8Ubv18vdP57U9NIdygE5mWQxCjXubG0Z15dvaOOtcemd6d60d3Dpwxsk6rZ/XjjW6bWHpfhtFuA13QvTBOS1rKs9yvn54lS5aweHFNaPbmzZsZO3YsACdPnmTRokUsWrSo+vpDDz0EwLZt2/jll1+w2WzExMQwcuRI+vXrB4BOp+Piiy/m119/Zf78+bRu3ZqLL744ZELSJUlyWShx9zPTqLTZMVsVKq12PlmRxYZDhZRZ7JwoNdf7S+tkudVlnhMNz/4+G44Uc9HQIgYHoR6XoOUjSRJn9m7DNSM78uGyrOrzPR6bC8DXNw5jaOeEIFkncMXJMotLsQPwzKwdtG8VweQeyYETq30vhrRB8P31kLO+zmXjls9hy+fOJ/tcDOf5d6lGoNFSnuWSqroKnD79ePfdd7nxxppfGIFcCtqeU+wy+Ze/Wf/o5Ga11NBcludO/SyFAoH4t3OXeXn1wxNJig4Lqm2NJdi2NeWzVJ/tiqLy2vw9vDa/bvbkKvq2i+Pn2xqR9bgp7P4D9i2AVf/1rv2kJ2HUXX41yVcE87MUit9JwUBkAwsBMlOiefvyATwyvTsdE+rODvkLmyN7qkDgC9wtmX6z5nCALRF4QpYlJnZPqrfNFcM61Hvd56z9AL640HuxA9BxlP/sEbQ4xIJoEHn8561uKxX7kosGtGFK71QUVXMYS4gyMbCDiGIQ+BadXHf5Y2y3RK4WuZ1CDotN4T/zdtc5v/i+cXRICFCSQYCt38N319bbpOyi77CnDQ3JGUBB80IIniDia7FzTt+2yBIkx4TRKzWWs/u2DfqUvOD0ocBFSoWnZvQU2btDkN25JSzcdbzO+Rd+38l/Lg5ggIMHsQMQ+c0FVEx6HkbdGgCDBC0Z8U0URLY+OYVt2UVU2jRH5ppNO66w2nlzwV5s3lQVBX7ZlON0fMeXG+iTGo1eljAZDOh1EnpZIj7SyBPn9AxOrRxBi8XV58nLj64gwPRKjWVmv7b8tNH5O+P3rce4Y0IZPdoG6AfSYydh12yoKNCOf7nDZbPwPx+EPx/UDrqfA5OfhMhEMEW7bC8QuEIIniASZdJ7jF65YlgH7vp6I4XlVuyKyo5jxS7rc7ljc7br4oA/rM8GYMOjk4lvRo7LgtDF7uKDGbAaTIIG8/JF/Vh9oICcokqn82e+vpSl9493GW3qc2QZup9VczzgSsj6CzZ/DYoCGz+re8+OX7QNYPwjWhmKMDGDLfCMEDwB5HBBOVa74uTroGU91nxrql4VFRRVRVG01/unZGqlJ1QVRVH5cFkWm7MLURStvVZzC06Ues7SfCq1U8vve+5Ml34YAoE3/HfRvjrnNh8ppE9aXOCNEdRL1oky9p8o5YlzenKsuJLHft7mdH30iwvJesG7WoC+MWgZHNsMhgjQm6DTWLBbod0QKvMPYdz8OXLp0br3LXwGCvbBuW8HzlZBs0UIngDx+5aj3PJ53fwSoYRNUdDJoZHPSND8GJnemk9O8Us7581lLH9gAm3jRFmJUKGgzMK4fy/y2O7HDUc4t3+a/w2qOAkfuS/w6T6hgYPht/nUHEHLRQgeH3CsqJKFu/KosNiZveUoaw+epF2rcHq1jQW0WZyiCneJAwPLFzcMpW9aHCa9HLjqx4IWzdGiCoY/v8DltZvGdiYlxuMjSxBA4iMMdE2KYk9eab3tnp21IzCCJzweLv0GvrjI+3v6XQ4z3/KfTYIWiRA8PmDMSwux2Jxz2hwuqOBwQUWQLHLPpe/VFOpb/dBEksTDSNBEVh8ocHvtncX76ZQQySVD2gfQIkF9SJLEb38fxZsL9rL/eBmztjgvFT10ZiaXDGlPRCBL0XSbAnesh83fQGUhrPKwRLXxMzjrP6AX/ocC7xGCxwd8f/MIPl2ZRbhBR1SYnkiTnthwA2q1L46K3ZEDx67U9tHR9u2q6vDd0Xxx6rRzc01VVX7YkN1ou4c8N7/OuTHdEunXLo47J3YV/jwCr5jRL5V+7eLYcbSEHzccYe62XKfrD/ywRQieEMOk1/GPMzK466sNda79tTefG8d0CbxRCV1gvCMSa+oL8NvdsO5D9+3/egWG3uxcPV2SQdRuE7hBCB4f0Dstlhcv8Fwl1ldY7QrfrzuCxa7Qv0N8dVFRFS178pO/bm9030t2H2fJ7uO87iLl/GuX9GNGv9RG9y1ouXRIiKRDQiRTe6WgqiqdHpztdP2hH7fw3Lm9g2SdwB2nhqWD9h3Q8YFZ1ccL7x0X2Gg7VYXZ99YvdgAWPa9tnjj7dRh4lW9sEzRrhOAJIpVWO72fmIvVrtKpdSTTeqU4ZntwzOI471dFbn29Njip+ltHmYIyrqB5YFdUiiqsTHh5UZ1rX6w6xLMze4nK6SHC2qwCLnh7hcd2Jr1MhNGPS1vFR+GVTG0/uZdWOf3oJt+OkZDu2/4EzRYhePxAcaWV6a8vbZAPz4ETZfyfi7DeQDOwQzzf3zIi2GYImhlnvbGUrdnFbq/fNr6LEDshRH1Z3j+4ehATMpMDY8jeP2v2c7d6bt9pDFzypRa+LougC0HDEILHD5itSkg6LJ9Km9gwPr52CCa9TJhBR5heh8kgo6qqeDgJGoTN7job5kfXDGZY54TAlSoQeMWrF/fjvjMy+G1LDi/O2eV07Yf12QztlEBkIEqCDLgCkntA+Umwm8FaoW22Sm1Z61QOLIHnU6H72dB+BJiiwBgJxqrXU/ejhTASVCMEjx9IjDbVSdqlVi1PqTVLVUqtJIOqUnOtznWlymm5pr1dUSmusHLvd5saLa6OFlVyxn+W1Ntm3t1j6Jos0rcL6mfOXWMAWHfwJOf/d3n1+as/XMNdk7py16RuwTJN4AKdLNE+IYJbxnapI3h+23yU3zbXTfK38sGJpMT6IaozdaDr84Ougx9vgi3f1L2241dtayyPFwrn5tMQIXgChCRJSBLI+PY/2dL7JwD+qbzeNjaMxGjhtyPwnqwTZXXODenUKgiWCLzBrqic07dtnTp8rgjoRImigLVc8+lxR3g82CzabJBq977vLhOabp+gWSIETzNh4a48rvlwTZP6eO7c3pw3IBW9LKGTJbFsJfA5f2w/5nQsarWFJvmlZl79cw+frqz7I2l6nza8demAwBuVvQ7e80KM6MPg/P9py1oCQQMQgidEsdgUxr20sE5hv8YwJr0V907tTnSYgfwyCya9jFEva686WQgfgc948fy+zN32R/WxXic+W6HIZf9bxc5jzoWF27eKIDbcwPiMJObvyGV8RhJyoHJx2SyexY4+DM78t1Yl3RCm1d8CrXBoikh5IPCMEDwhiqKqPhE7AEv2FrDkzWVetZ1z12gyU0TlYUHj0J0icKLDDEGyRFAfH10zhGHPOycePVRQDsC932ph4U/P7MUVwzoExiC9EZJ7Q+4W7Th1oDbjUxtbJfxyu+v7L/seuk7yr42CZo8QPCFKmEHntlpx7aRgvsSol4mPEMsPgsaz+UhhsE0QeEFKbBhZL0zn7Df+Ykt2UZ3r8REGLhwYgDpatbnlr5r9o5vgnTHe3TfoWug4yj82CVoUQvAEgcJyC6AlFFWhTmTWqa9VZSSqXu+bksGPG7LZf7wUxXU0sFd0SIhAJ0voJM2nR6+TuOnTddo5WUIvS0zITOK6UZ3EspfAK57+bYfT8ZsL9hAXYeSCgWkiND1EqLDY+XNHLjHhBiqsrp19T5ZbmbvtmH8zqxcegtLjIAHlBVodrc7jNAfkvB2e7taITIJpL2qlJQQCDwjBE2B84XzsKw7ml3tss3xfPjP7p4osywKvOK9/Ks8erUlA+O8/dgOwN6+UJ87pGSyzBLV46rftfLn6kMd2P6zP9p/gKT0Or7rwu3EVgl4fZXlaNJcu1jd2CVo0IiNTgBnQPp5hnVsRHRbaWnNwx3g+v34oi+4dJ8SOwGuW7ztR59x1ozpx92SRhydUuG9KBmGG+r/6R3RJ4ONrh/jPiMjWmgNym75a6Yc4R3HZhpaBuHcvhAmxI/CO0H7qtkBiww18deNwr9oWVVjZml3ExsOFvDR3l+cbmsBPt40k3KBDr5Mw6mTS4sPFMpagwbRvFeF0/PblA5jaq02QrBG4olWkkW1PTmXMiwvJLqxJWqqXJVY+NJEIo45wfy8/ShIMuUHbqsjZAO+Oa1g/O3/VfHgEAi8QgidAHCuq5ESpGUVVkU5JPqhSk0VZrZV9+ZJ3VwbMvplvOUdx/XL7SPqkxQVsfEHzx2ZX6iS/nL8jTwieEEQnS05iB8CmqCiKSoQxgI8FmwUW/wsOLodkL5Y8L/oE4juBqoAhHBIz/G+joMUgBE8AOFZUWScENNSZvyNPCB5Bg3hu9s465/Q6sWoeSszafJTbvljv9vqK/fn+dVS2mWH561BRqGVR3vwtlDiyPB9a7vIW1RBByR27iIkR6TIETUMIngCQFG3i7xPSmbcjD52sRWdty3FfWdpbzuydgkEno5dlDDotssqgk6sjrkorbVSYLegkCA8zoZMlZEmbX5KkmnIXEhKypJ3TSRI3jOks8qcIGswHyw44HZ83IJXHz+4RJGsErsgvM9d7/fOVh1BUlXP7+ykkfcOnsOCZBt0iWcuJeaWdlniwVeeaC0NuhEHX+NhAQUtGCJ4AIMsS95yRwT1n1Ey//rk9l+s/WevULinaVL2cVVUgVFVxLHep1SHqdkcs+uwtzmn8fUVOUSX/Or+PQwQJPx6Bd3xxw1AufW9V9fEP67PJOlHGD7eODKJVgtpcObwjVw7vSJnZRs/H59a5vjqrgNVZBZzVpy0Gf8zODbxGEy7l+aDYHLk5VNj8FeTvrf9eWyXkba85/u0uIXgEDUIIniAxqUeyU2JBVwIoWHy37gjfrTtS57y7RIiC0xdFUbHYFbblFDmJnSr0soyqqkI4hwDeJix99Kwevhc7jXFIro+b/4LYdr7rT3BaIARPALHZFb5ee5gKi5bsy2JXSI4Ow2JX+M+83UG2rn7uniTCigXObDpcyIy36i9Z8un1Q1BVbblUEPpEm/RcN6qT7zvOq+vf1WAeOa6VoBAIGokQPAHko+VZPDPLywyiPmBKz2TWZhWQX2YF4F/n9+biwe0DNr6gZeLJ8bU2GY/Mqd5/94qBnNEzxV9mCTyQmRJdp2DoqZSYbR5ngkZ3bc2n1w31fuCsZfDTzd63d8czifVfH3ozTPtX08cRtFiE4AkgVwzvQIXFzuerDnGsWCsM2jkxkrax4dXlHHSO6sSKomJVVOyKgs2u+fPYFBVbrWO7orL/RJnb8eZuy3U6/uf3W/jn91uczkUaddhVlbsmdePmsV18/BcLWiJHiyo8NzqF1lFGeqaKBHHBZNbfR9PlodlN7mdwx1YNuyFtsCZGcrdB1tKa8+MehPB4zYdnzj+bbBeW0qb3IWjRCMETQEx6HXdM7ModE7t6bKvWclCu7cSsKGB37JeZbdz//WZWHyhotE1ljuW1F37fKQSPwCuuH92Z60d3rnM+u7CCkS8sqD5+YFomN47ujCyL9axQQCdLZL0wHbuisjargIvd5Pm6ZVwX/jk103cD642eZ17aD4XPztecmb3hzs0QH6BK7oIWgxA8AWbnsWKmvrrUc8MAY9TJdHvkd768YRgDO8QH2xxBMyQ1Ltzp+HBBuRA7IcjqAwX87T33SU2Pl9Qfuu4XTh70XuwAvNanZv/WVZDkQ4EmaLEIwRNgEiJNGPUyFpsSbFOcsNg1e75bd0QIHoFH8kvNvLFgL1bH50ZFW5mozeerDvHsuS4KRAqCSq/UGCZkJrFqf371DG9tuiRGBd6odk2o21WwTwgegVcIwRNgTpSaGdqpFeEGHVa7gt1RTqLKJ2dVE5anGsOlQ9vz8JndtYSEEpj0IjOuwDP//mMXX64+7LHdjqPFdG8jMuSGCr9syuHvX26ot82Vw/24VLTk37Dg6ab302EUXONdmL1AUIUQPAFm2muhtZz1xapDfLHqUL1tfrh1BAPai1kfQQ2PndWTXqmxKEpNzPmjP22t067cYgu0aYJ6+HN7rsc2PR+fyz+nZnL96E7oZcm3OZR2/OKbfsbeD4dWgqzXNn0Y6AwgyVrJClkPkk77bKoKGCPBFO2bsQXNFiF4Aswvt4/kxw3Z9G8fT6RRp2VSVlWnoqFVGZZP3X/ghy2eB/AD5/2fVuNm8xNnECNKTgiAcKOOy4Y6zwRsPFTI9+udE1ae/98VAAzsEM/3t4wImH0C17z+t/7szi3xGJ7+rzk7+decnQxoH+fbTNk3Lal7TrFr0VufzPC+n0/OafjY9+6BqKSG3ydoMQjBE2D6pMU1uCjn4YJyft2cwz+nZjpqX2lk5Zd5tazgC8ZlJBKm1wVkLEHz5OWL+tYRPFXohfNyyPDaJf2ZteUor8/f47HtzP5+LCRahayDTmO12ljZ66FVJ9jyref7JNkxiyMDquPYcU6Wna/3OAfCGxhOL2hxCMHTDJj66hKXzoW+IinaxP+uGiSqowt8yvTebXjrsgHBNkNQC7uiMuVVF7MsLvjw6sGMzwzQjIi5BFa/q+1ne1liZ9qLMPBqbSlLIPACIXiaAT/cOpIfHL+cqwJhVEdITFVkTJnFzper6/fFcUdeiZlz3qy/RADAI9O7u8y/IhCA9pkc3jmBFfu18OJZW45yx7FiMlOE03KooJMl3r9qED9vzOFgQTnjuiXympuZnr15pYETPGEx8LevtOKg0W2gvAB2z4GKQsh1s5Q/+15tq83kp2Hk3/1urqB5IgRPCLI7t4SPlmdhOGUZ4JSoX6cwYL0scdnQ9jgKqWPSy9gVlU9XHvSZXQfqyeosECzbm18tdqo4mF8uBE+IMbF7MhO7J3Mov5ybP1vntl10WAAeD4oCS/8Nm7/RqqXHtYfUAbDtx8b1J4tHmsA94tMRQJ7/fQfvLN4fbDNc8ul1Qxjd1UOtGoGgHly56ZzRIznwhgg8kltcybTX3C+VXzq0PZcMCUDdvZKjsPDZmuPCg9rmict/gPSJ/rNL0CIRgkcAwOwtx9DJErIkVb/KErX2JWQZdJKEXNXOkbunuk3V9eo2NdckCe0VkKpf8W3IqyCovL6g7tKIeH9Dj8MF5Yx+cWG9barSVfzn4r6c2z/Nf8bEpkKXCbBvgee2tfnsPOfjc96AAVf6zi5Bi0QIngDy4LTuPDite71tisqtXP7+KrZkFwXIKo0vVx9qtA+QP3jozExuHCNqezUnUuMiAOfEmb9tzuGsPm2DY5DAJW3jwrlpbGcW7sxjd279BTdbR5n8b9AVP0JlEcx5CE4egIOe/Qnr0DrD93YJWhxC8ASAk2UWLnpnBXvyQrOar0EnoapV5QFUl2UCAs2ILq2Da4CgwbgKS19/sFAInhBDJ0s8OK07d0zoSq/H59a5/vblA5naKyVwBuXthP8b6n372HaaY3PPmTDs1urElwKBJ4TgCQD5ZeaQEjuju7ZGJ0vEhRt4ckYvYsNFWKeg6RwtqnA6jg7Tc/9U8cs7VIk0us6rdfNn6zjw/JmBW45saAbkosPadmQ19L8cwmL9Y5egxSEETwBIT4om64XpTudUVa3Osjz8+fmcKLUExBZJco62OusNrdSFhMRrl/SjvyghIWgkc7ceczr+/pYRhBlEsspQpbDc6vba4Gfn8+UNQ+maHIByDLGNTG4YHk9NGlaBwDNC8AQJqcqRF4mvbhzOpFcWA5CRHM2hgnIiTXosNjvFlb6tRaSqcORkhctrby3cy/+uGuzT8QQtlyW7j/PUb9sx2+yYrQp5JWan66VmUUcrlIkNN3DflAw+Wp7F8VPeuxOlZib/xzlB4Xc3D2dQRx9nKz6+Gw6vggs/hj8ehaJT/AgjElAri5AUF5+lipPwQjvX/d6yHJJ7+tZWQbNHCJ4AselwIWuyCigst/Lmwr1u2+3K1WrcVFj9k1l5as8UHpiWiSxJqKhIaK8AqXHhfhlT0DL55/ebOVpU6fLam5f2FwVnQ5hyi40ej9X136mPVQcKfC943vLwA6s8v3FzOEc3CcEjqIMQPAHArqjMeKsRkQdNYPcz0zDqZYqLiwGIiRHJ3wS+ZcE/xnH4ZDkSUGlV+Necnfy19wQAt3+xgX7t4kiLjwiukYI6mG12ep7irJwaF867Vw7EqJPR62T0soRBJxNu0BFh0mHQyb435Ij7pIcA3PwX6IyUllegSjqiY2IddbJ0tV4d1dF1JtAbfW+joEXhh0+x4FR0ssT5A/yYy+IUOrWOFIELAr8TbtTRLTmarsnRbMspqhY7VSzcmRckywT1UVppqxOFmV1YwabDRXRNjqZT60jatYogJTaM2AiDf8QOQNnx+q+/PQq+vQYlMgk1roOWhTk2FaJTICoRIlpBeJzm9CzEjsALhOAJEDeNDVwNqgMnyuj68O90fGAWfZ5byo1fbOGpX7djsSkBs0FwevHAD3XrHZ3qFyIIDRKiTOx5dlqd8w/9uIWfN2YHzpCMqfBEEST3dt8mbxtR749Cd3RD4OwStFjEklaA6JZcN1ILtBw9/Z+e59exV2YVsjKrkLEZiYztJspHCHzPtiencKy4kokvL64+9/qCvdw9uZvIthyCbM8pdnk+UNGigJZs8AXP5Svkinx0h5dDxtgAGCVoyQjBE2COl5i57Yv17M0rpaCs8V8uQzq14paxzpmI1VrlRSWHq19ZeTkAbVvHCidSgd+INOlJi6/r9N7pwdmc2z+V/1zcL/BGCdzy4bIDTsff3TycHm1jiDAG6JGgqvDzba6vpfSB6S9DTCrIOoqtMujDCAuMZYIWjBA8AWL5vhNc+t6qJvUxvU8bwvQ6R10qmLP1GLIMoNWtOrVelSxJWK0WJAn2rjvG8n35XDGsA12To6prZulc1L3SyRJWu8L8HXnERRhIjDJx49jOmPQip4rAPXNOycNTxfTebQJsicATP23Mqd5vGxvGi3N2EWHSEWHUERNmIL/MQlGFlX9M7obJoKNzYiQxYT5MUGq3wuE1rq9VnITf7gadEfQmIlRZq4JuDNNeZR3oDI59x3H1vkE71pscW7j2agjXHJtr36fTgykW0gaJbM2nCULwBIjkGPe/T7okRmLU66qTEaqoKGpNckLFUe5h2d4T9SYL84ZPV3pRidgFYQYdN4wJnB+SoPnRrpXriKxtOcVMElXTQ4rZfx/Nma9rSUdziirJcZNe4OJ3VwIQH2Fgw2Nn+M4AvRHu3QWWMlj3Mcx9UDvfuhuUndAyKVc19d2orjnvPehzkb9HEYQAQvAEiC6JUS59eLyl4wOzfGiNeyKMOjomRFJusZGVX07nxEiuHdmJSwa7SfAlEABfrT7k0nEZoFNiJBabgkEnCX+eEKFH2xi+vGEYf3tvZb3tMpKjsSoKT53Tyz+GGCNh+K3aBtrMz9NNqKPXY6aWgVnWactmihXsNlBs2r5iq3Vs03L1dJvqkz9FEPoIwRNirD5QwEXvrAja+E/N6MUFAwMXQi9o/mw5UkRusfuIrL9/6TrCZmy3RD6+doi/zBKcwrK9J7jsf94vqz98Znf/zOq+MRDy3SdfbRJj7xcJBwVuEYInCHy+6iAP/7g14OOO7ZbII9O7Y9RrycUMsqQlGdNJGGQZg047Fgi8ZeX+fC55t/5ZAndM6p7kY2sE9bHjqOvIrFPZ++w0/3wPHN/tObOyG2ztRqJPTIe9f0Kv82DozQ7/HGONb46YPRR4QAieINA2wCUcWkUY0Otkdh0r4aoPViPLEndO7MrZfds6OS4LBA1lYId4zurTht82H3XbZnrvNrx12YAAWiVwhXJqtkE3pD/8O3ERBjY8Otm3S5D5exp9q/7wMjjsyFa//A1tA7hlheaUXOW4LOkgRjjJC1wjBE8QGJ+RVO3PU26xsTu3FL0sUWa2UW6xU2bRXsvNNmyKil1RsasqdrvKZ6sO1rt84IoCF47O9323mfu+2+z2njCDzM6n6yYnEwhqY9DJ/Ov8PmzPKWb/iTKXbebvzA2wVQJX3DimCzeO6UKFxU5BuYUFO/N49CfXM82F5VYufW8VH14z2HcV7zOna4kGa6Oqmt+OYnW82rTXkhz44mLP2Zj/O7zuuemvwODrfGOzoEUhBE8AsdgUlu45jl1RMRl0GB3TxqrqEDSKioqKSa9DL8tEmfQojvPaK/xjcgZ2VTtWFJVHf97mF1vP6tPWL/0KWgYnyyy89McurDYFu6rSt12cW8Fz35TMAFsnqI9wo44de4vdip0qVuzP54ZP1vLpdUP9Z4wkOcpCGLWCnx+dDeYij7e5pe0AzXFZIHCBEDwB5OV5u3hn8f5gm+HE1SM68thZPcSSlqBBvDh3F1+uPlRvm9vGd+GeyRnoxGcrpPjPvN28Nr/+5aX0pCiSok08PL17YIzavxg+Ocf79n0u0cSSqoKqaFuv8yHzTP/ZKGj2CMETQK4Z0SlkBM/4jEQ+vEZEyAgazvacYhTFsz/IWwv3cd2ozrSKFIUdQ4mNhws9tumdGsu0Xil0SYzyvQG//xNWvd20PrpM0Ja/IltD1zOEw7LAK4Tg8QN2ReXDZQfIKzFT/d9QImTEDsDCXcfp+MAsPrtuKKO6NiHvheC0Y+Zby7DYvStEO8BRJy41Lpwl948Xsz0hwPiMRBbvrt835scN2fy4IZvrRnXi0bN6+NaA8vym9/HjjTX7f/sKMoS/ocAzQvD4gTlbj/HMrB3BNsMjnVtHkpESHWwzBM2MhfeNY962Y6jAh8uyOFRQ7vGeUrMNVVUBIXiCSYXFTnZhRb1tMlOiadcqgiiTnpv8kYfn/P9BRGtY9d/G3d/zPJBkLRS9VRdIn+xb+wQtFiF4/MCZvVN45aK+HC8xo6ItM1da7R7Xzf3B3NsG0yY2jJiYmICPLWiZpMaFc/XITgAMaB/PjLeWebzn3SsGihxPIcC015aQle9eoF42tD1Pz+jlf5++qc9DWAws/lfD7jvnDRhwpX9sErR4hODxA5Ikcd4A52zFWSfKgiJ4przlpkBfLUx6mbWPTCLal8UBBacF87Z7F3K+/lAhQzsn+NkagSeeO683z8/eya7cEiy2usuSn686xOerNGf0ppTC8YgkQd9L4NAKKMqGgn3e3ffLHdomhI+gEQjBEyA6to5k+1NTKLfYsSsqNkXl5bm7+GFDdrBNw2xTsHvhhCoQnMo9k7sRFaZnX14pu/NK2eTGIXZLtuvzgsAyoktrfr1jFJe+t5Ll+9z70vhlKetUWnWGq37V9hU7PNXK+3tF+QhBIxCCJ4BEGPVEGGv+ya8Z2Slggueq4R14coafCgAKTkt+2pDNXV9v9Krtol0eEsgJAsoj03tUV0t3xTtL9vPOkpogi51PT/VdAkJXyA3s+70JnttMewmG3ui5neC0QQieINI7LbbOtPG1H61hwc48n4/18YqDfLziIJkp0ehkCX1VHS1ZQq+T0Mu19h3ndbKEhIQkQZ+0WK4Y1kFUuz5NKTXbGPnCAooq6mbt9sQTZ/dgRr9UP1glaCwLdzXsOybz0Tl+sqSGrDAfd/j7fdoWIjTai7JNX7hpiS9NOW0RgifILNyVx/tLD1BmsWFXVDYfaUKWUS/YeaykUfd9t+4II7okkJ4korpOR2x2pVFiB+BYsZl4kYsnpPh9q/vaZ4HEgI13DS8zXrcp2KaELiIKzWcIwRNEVFXl+o/Xhpz/zAvn9SbMoMOmqKiqigp0SYwSYuc0Ji7C6NaJde62YyzefZwvVtXNvNyvXRz3Tcnwt3mCBjKjbypbsz1XT//0uiGM7proP0MqTsK/fOR8fN2f0K5x1dgDQXGx9u8tImaDh18Fz+rVq9m4cSN5eXn06tWLmTNnAlBYWMhrr72GwVATFTRy5EjGjh0LgM1mY9asWWzfvh2DwcDIkSMZPrymSNz+/fuZPXs2RUVFpKWlMWPGDOLi4vz5p/iNUBM7AD3axtAnLS7YZgiaAUeLKrjp03Vurx8rqhTJISSLWgAAXoBJREFUBkOQwZ28cxDWyzL5pWYSokz+McRm8U0/ST0gPF6L+JL1EJEAOvF73le0lGe5Xz8R0dHRjBkzhr1792Kz2epcf+CBB5Dlurk5Fi1aREFBAXfddRelpaV8/PHHJCYmkp6eTnl5Od988w1nn302GRkZLFiwgO+++47rr7/en3+Kz8ktrkSWJNLiwzlysv5EYIHmnDfr5lVZ/fBEkqJ9vcguaC6sySrgWFElAKdK9IsHtWPNwQJkSWJvXqnTtaGdGxB5IwgINrtCl8RIzuiRzB8e0gr87b2VgJaf59lze/vGgJwN2syOzaKVh+gwEg56zuVUL3nb4c2BzucGXw/TX25avwKg5TzL/Sp4unfXCs/l5ORUT+d5w6ZNm5g5cybh4eGEh4czYMAANm7cSHp6Ojt27CAxMZGePbWwxHHjxvHSSy9x4sQJWrcOnRIJr8zbzetByLvjL/QuPsyClsPF76xg1YECn/b5zMxeXD6sg0/7FDSNvXklTHql4Q6wE7sn+caALd/B99f5pi9PrPmfEDw+oqU8y4M65/fqq68C0KVLFyZPnkxERAQVFRWUlpaSnJxc3S4lJYVdu3YBkJeX53TNaDQSHx9PXl5eSAgeVVXZeayEj5dn+aS/p2b0JDbcgCxJjk1LbChLaMcySEgggc7RptJqr66lV15ejqJCeHg42uqZiqKCoqqojtdWkUb/rtMLQprjJWavCkp6y2uX9GNCZpJIZBlClJptjHlxIQVlnpeQOiZE8NNtI4mL8IOjeeFB3/dZRYeRMO5BaNsPTMLfMJA0l2d5UARPREQEN9xwAykpKZSXlzN79mx++OEHLr/8ciwW7T9kWFjN8onJZMJsNgNgsViIjIx06i8sLKz6vmBz4dsrWHvwpM/6e+znbdX7jcmILBzlBPXx+aqDPPzjVp/2GRNmEGInxJDQ6mh5Q1Z+Of2e0oq+3jS2Mw9O6+47Q1p3811fVdy+Flp39X2/zZx169axbp3mX1de7rneXWNobs/yoAgeo9FI27ZtAYiKiuLMM8/k5Zdfxmw2YzRqvyrMZjN6vb5632QyVd9b9Q9WRe37gs29UzK45N2VRJv0dGgd4VUkhLeYbQo/bcwhPkKb8ZHQ/CkUVZu1UVVV21dqZnBKy8tRVTCGndQirhzttPu0/YEd4unfPt5ndgqaD5O7J/PFqkPsO15KpdW7CuiemLcjF71OYlR6a5G3KUSINOnZ8fRUANZmFXDB2yu8uq9/Ox9/L3Q/27f9gVZqIiZVW74Kj/N9/82UgQMHMnCg5tf07rvv+mWM5vYsDyk3dlVVCQ8PJyoqimPHjtGlSxcAcnNzSUzUllySkpLYtKkmZ4PFYqGgoICkJB+tMTeRYZ0TnMJ3n/x1Gx8uy/JZ/4/+5Ntf41X4PZOqICRJiglj1t9HA1BusdHjsblN7vOLVYeqQ9RvHNOZs/u0xWJXiDLpyUgRSw3BZlDHVnxw9SCu/Witx7ZTe6X43oDr58OBJVo0VVw7MEbDT7dAWSMTrh5yiLf2w2DIDb6zU9BoQvVZ7lfBoyhK9aaqKjabDVmWycnJISwsjISEBCoqKpgzZw4dO3asnvrq27cvS5cupW3btpSVlbF+/XpmzJgBQGZmJvPmzWP79u1069aNxYsXk5ycHBL+O654/OyePH62c90XRVGptNmptCrVMzE1sy4qj/60lT93+D7bsjt6pcbw+vw91ePLksQt47oQI5YlTisijHr++ud4DlVV03ZMIbpKnCCh+ZIpqopdUbGrKuVmO7d9sd6p3btL9vNurRIFP946QswmBhmrXSEjxf0S9z2TuzG2WyLtWkX4x4C2AyC5F9gtYLdqr9f/WbO/+AXY/nPd+zqMBJ0RbGY4tFw7N+oeTeiYYrRXgV9oKc9yvwqeJUuWsHjx4urjzZs3M3bsWBISEliwYAFlZWWYTCY6d+7M+eefX91u3LhxzJo1i1dffbU6dj89PR2AyMhILrroImbPns2PP/5IamoqF1xwgT//DJ+w5UgRBeUWFMeDYVduCZ+tPMio9NboZAmrXSvgabVrD5DRXVtjc+xbldrXFGx2rfioza5gU1SsdoUysx2LvXFLEluzi+ssvRWWW3n+PB+FoQqaDWnxEaTFu3/QlVtsvDhnF8eKKklPiqJLUiRWu4rNrpJfanZ7H2gFKXunxvraZEEDGPfSQrLy3ftzvHpxP2b0a+u/pcgja+F/Ext37zWzfWuLwGtayrPcr4Jn3LhxjBs3zuW13r3dP0z1ej0zZsyoVoKn0rlzZ26//XZfmOh3VFWl04Pu/6P+sinHYx+9U2NpHWVEJ8sYdFK9tbB0OgmDLKOTJQw6iZMlWpSWyWRyRHhR7f8jOUV+aceSI9rriuEinFjgTIXF7rzktc19W4DhnRO4dGh72sSG0Ss1ViyZhgAz+6fy6p/u02Xc9fVGJvVIJsrkp0dDfEdI7g25Wxp+r90mkgkGiZbyLBefHj9gsSlsyS7k/P965xjoiWfP7dXozMciSkvgioIyC8/M2o7ZpoCqLXMUV1qx2VUqrHYqrHb6psUhAZU2O7O3HCPC2DDB8t/LB/gntFnQaO6a1I1yi91pmfFU8oorsUeaMBlkTHrZt7M95QWNEjuqPhzp+E6q820k9ajZFwi8RAgeP9Dtkd8bdd89k7vRISECo05Gr5PROXLs5JdZWLBTy4iq+fvU3KOizSLVPq5qBzXhiBERZdq9tcZTVZAlmNQjGYNOJBY8nXj/r/38sD673jb7j5c5HZd7EdZ83oBU+qbFMbBDvBA7Icqo9Nb1Cp4JLy92Ov7znjG+q6MX1rgfXpKtAt4eWXNi5F0w+Unf2CQ4bRCCxw/cNr4Lby3c1+D7Xpm32w/WeObK4R14akavoIwtCA53T+pGn7Q4LDYFSYKPl2exJqvp+aN+WJ/tUkjdPzWDW8elN7l/QeP4aUM2d329scH3De3UipTY8KYbUFkM70+G4zub3lebfjDomqb3IzjtEILHD9w3JZP7pmRitSuUm+1YFQVFUfllUw7PzNoRUFvax4eRnhiJwaC91Zr3joYkgSxL/H2iSNp1uqHXyUzpWRNyfFYfLZeGqqqsPlDAxe+u9Ol4fVLjfNqfoGHEhnsXcTmpexL/nJqJJGkO7D7zu9IZIDLRN4Jn/MOaL5BA0ECE4PEjBp1MbETNUtF1ozrxyrzd9S4NXDeqE4+e1cNnNggfHkFDkCSJoZ0TWPCPsRSUWbApKifLLKzOKqBzYhQWm0Kl1c5Lc3c1qN9RXUMzbcTpwvjMpOr8YPN35HLdx65z8JgMOrom+yFXkiEcrv6t7nlVhSfjGtaXYvWJSYLTDyF4AojZpnj0gzi7b9sAWSMQuKdzYhSda5VXm9a7jdP128an896S/Tw72/2MZf/2cXRvE8OFA9P8ZaagEbhLDXDTmM48eKYPy0h4Q9lx99cu/Ra6nSF+tAl8hhA8AeRYUaXbawPax3FGzxTWZhWw6XAhekd4uV6nhZ/rJAmdrIWU63VaOLlOlrSCobK2rygq5VZ7dRFRgLJyzVm5TYJC33ZxAfpLBacDlw/rUK/g6ZIYRVG5FbviKnWhIFgkxYTx3c3D65SX+GLVIW4dl05sRAATjkYlweSnYN5jda99cSEAbmXOqHtg0uN+M03Q8hCCJ4B0bB3JzqenUmGxM/yF+U61i9YfKmT9oUK/2/DvC/tygfjFLfAB4UYdf94zlnPfWkaJ2Vbn+nfrjgAwZ9sx9j13ZqDNE7jhpw3ZfLHqEGf0SOaP7bnV50vMNvo+9YdTaRy/o6oQlQITH4Mdv0LOBu/vTRvkP7sELRIheALM/y3ax+vz3Sf+8je5xZWs3J9fnXgQNOfluHCDf9buBS2a9KQoXrqwDzd/tt7l9dS4cMZmJHIwv4wOCZEu2wgCx+GCco/RWh0fmMVvd4yiVyCyYm/9Hn680bu23aaB3qQ5QOuMsOcP2L8IWnWGITeCLBJbCupHCJ4As2R3PWvWAaA+Z9MPrx7M+MzQKMIqaD7M2XrM7bXsworqYqJXDOvAyPTWnNEjGVkWSeMCTVG55uzbJy2WzUeK6m174ydrWf5gI0tAVGEph/ITIMnaVlWcTbHD2g9g9xytLpa37K4nv1l8J8iY2jR7BS0eIXh8yJYjRWw/WoTVrvKvOTspqXSe5m8TG4aiqrSOMqGoanXhRcVRfFFRtcKiNh/6PAzuEAsqyDqdlnTQ0bXq2KlKRpieGMWI9ASfjSsIXRRF5fetxyiptFZ/7ux2Batdq9tmtanYFO14W04RS/ecACAzJZpyix2zzY7VrmKxKdrmZQ23T1ce5NOVB1l6/3j/FaYUuCS3uJKhz82vt01qXDjhRh3t4sM5u29bluw+rpWrkWv8BvWOsjUAZRYbafHhtHGXp+e5Nq7PNwIlOhU5KRP0YdqUtKpoxUYVG0S30QTVnj8dNXLkWq8OoVW1X/s63raVtRklnQHC4kAvEmo2V4Tg8RF5JZWc/eZf9bY5Wo/Tsr9Yc7D+X3JVrDt4kq/XHq63zeNn9+CakZ18YZYgiHy26iCP/eyhEJYLdh4r8arddaM6kRBldDjaS46abZrDfXpSlBA7QSAh0sjVIzry88ZsTpa7DuvOLqwAYG9eKQt3eT8TvffZaehdZWqf/gosfQWKj9Sc05kAVauK3gDkkmwoqScz+KYvGtRfk3jCu+9UQeghBI+PSIoO4+UL+7Jifz4GnVbkM9yg4516Urg3N1JiwoJtgsAHXDSoHUdOVrA1u4jl+/J93v/7fx0gNS6c964cRPc20f6rvC3wGr1O5olzenLnxK70f3peo/rokBBRXXhYr5OIMum5ZEh712IHYPB12uaK0jz4tx8SniakO9feqV1Mx935+q6paLNJql2bURp1t+9sFQQcIXh8yPkD0zj/lAioOduOcTC/PEgWec+dE7syNiMRvaxNXUeH6YWTaQslzKDjoTO7e7XM0ViyCys48/WlPHRmJjeO6eKXMQQNJz7SyJuX9uf2LxoQDQW8eH4fLhrczneGRCXVnSl5oolO0m36wU2LPTYTnL4IweNn5t09ludm72B3bgk2u8qW7CIqrJ6LMAaa1+bv4bVTosc+uHoQEzKTg2SRwN8kx4TVG4KsOnzM7KqKooBNUVAUsKuaj8+3a494zLgsi9mdkCCnsIIRLyzwun3fdnH8fNtIzw0byt758Nl5vu/3vv0QKXwQBfUjBI8fGPH8fHKC4K9Tm/EZiUiShM2mOU7rdM4hm1qVde3VbLVjUxwPN8fWJSmKEV1EOYCWyMfLs3j8l4b78DSGl//YzfWjOwdkLIF7jHo3y05u2HS4kMxHf2fW30fTJTHKd4ZE+SkK9KVan7H7D0BEK/+MI2jWCMHjB7qlRAdd8DTE6dAV248W8+umnOrjW8d14b4pGcIfowWQnuTDB1g9jOiSwMfXDgnIWIL6aR1lqp7NU1WVr9cc5j9/7ia32H1YeKVV4e1F+3jpwr6+MySld92lrMLDsOAZzU9GsTtqZUlaVNTW7xs+xok90H6oT8wVtCyE4PEDH13j+kt+3cECzv/vCpfXQp3//XWAeyZ3Q68Tgqe5MzK9tcdsuqqqsv5QIef/d3mjx5mQmYTBnUOrICiUmm0s2pXHgRNl9YqdKr5dd4Rv19VEWX187RDGdkus544GUJavJQ9U7dBxlONkVd4MFQoPNq7fD85wff6CD6GXH5bTBM0GIXj8gKKozNpylHUHTxITbsDoqIdVXy2tUGLvs9Oqw4kFpx+Ldx/nqg9WN7mfS4a094E1Al9y9QerWXvwZKPv35tX2jTBs+0n+Paqxt/fFIrrCWsXnBYIweMH8krM3PFlw6Ig6uO+KRmOYqGaE6gkSegkkB0RVXJVnhNZqtPOUlmBJElERUZUl5OoCis16GT0soRBL1cXKm0bF+4+zFTQotlw6CQ/bsjmkxUN+2U9Kr0114/uhKKqlFTaKKqw0qNNDFEm8fUSarxwfh8mveI+kmnlgxMx6mVMjs3n3wUxbX3bX23G3A8THvZf/4Jmj/hG8gO5xb6byRnYIZ7bxqc3+v7i4mIAYmLc1hwWCAA49/8at3z15IyevnVsFfiN9KQoHpnenWdmua5y/9vmHC4c2I5If4nVdkM0H57/GwF5PnacX/pv2LcAxj0AXSf7tm9Bi0AIHj/QKzWW7m1i2HG02OX1pGgTkiPzrDYTg2NmRnLMwuDYlygz25j++lKn9lArNZaqVu8rqorNrnJGj2TumtRN1CsSNIhfbh/JvO25KKrKWwv3eX3fxJfdzxjEhOmZe/cY9+UHBAHn+tGduX50Z3YcLWbaa0udrj0za0e1GPr9ztF0b+OnH0o3L4WKk1p19M8v8E2fqgLZa+GrS+HR4NYsFIQmQvD4mGdnbee9pQfqbZNX0oCCeY1g57ESrh3VibgIUfNF4D190uJoFWlk1L8W+qzP4kobR05WCMETQlRa7ZSZbXy1+lC97aa9tpTpfdrw1qUDfG+ErIPI1tpMzBNFWhbjZ9s4IrQawTVzaupeJWb41lZBi0EIHh8TqJBfgB9uHeHklyNJoJdlkmNMQuwIvOZYUSVWu4JdUckv850Yf/PS/pzVx48+G4IGs+94ab0zcqeS46iv5TNKj8MfD4O1HKwVsPfPpvV32xpI7OYb2wQtHiF4fEhucSX//H6LT/s06uTq5azqV+CmsZ0Z0D7ep2MJTj9cLWs0hegwPZkp0XRIiGSiyNIdUhRXWqu/R1TVc3uADYcK+WRFFlcO7+gbIxY+A5u/9k1fAG8Ngbu2QJwPy14IWixC8PgQq11p9L03jelMdJieQR1bMayzSJEuCAxdk6K4bGh7Zm05SkmlDbvi5ZPQDSWVNv42pD3nDUjz3FgQMMw2O32e+KNR9y7dc8J3gifO16kKVC3cXAgegRcIweND0uIjqhO6VVjsdH9sjtf3NrWq+vtXDWJkemsn52bhsyzwhF4n8+y5vRneJaHBBSXdcc83m7jnm02kxoXzxQ1DaR1lwqCTG1zeQOA7jDqZ+6Zk8NnKg9gUleMe/Ag/v34oI9P9UFqmx0xY84EjJ07TxDUAEa1h+89aja7R/wBDWNP7FLRYhODxE+FGnedGPuS6j9c2+t47JqRz7chOxEcKv5/TFX/kzMkurGDsS4uqj7+7eTiDOooaR4GmsNzCyv35dG4dyaNn9UBVYWtOEf9d5D4S77L/rWLp/eNp1yrCt8YkdIF7tmklJD46Cw41PpM3AOUnYOX/afuRiTD0xqbbKGixCMHjA5bsPs6VLjLTRhh1lFtCrzL6qbyxYC8VFjuPnNUj2KYIAsjRogqGP+99Be2mkhIrfn0HgymvLvGqjERt/nNxX9+LnSoUBZ7yofAdfju06Qu9zvddn4IWiRA8PiDS5Ho2p9xiJyM5moQoI0a9jFEnY3C8avsSellzStZJErIs1ew7lqQkR36eU/dPzbis7de+puXyMVdWIkkQFRnh1GdppY1yqx1FUTHoZM7tnxrgfzVBsIkOM9AxIYKs/HKv2vdsG8PkHskYdHK9n8PCMgt78kq5YGAaraNM9Ggrkl4Gk/9dOZi3Fu6ldqUYSQIJiY2HC8k+JRLrlnFdmNnPj98HkgQDr4Z1H0FMmhaxVVHgtrmtzUDMo/5JZMdBoNODbACdUQttF+VvBA1ACB4fMLBDKw48fybfrD1cJ0prV24J5Da8z/evGsTE7k2PchGZlgXuWH/wJNeP7ozFpvDUb9s9tt+WU8y2HNfJNAHWPzqZVmJZNOTonRbL21cMdDq3LaeI6a//5bL9fxftw2JTOKdvW5JiTL7PoSRJ0HWKJniKj3hsrj+6Dv23F8FFn4DNAorNccXhA9RjBhgjfWujoEUiBI+PkCTJpyHpHRL8NJ0sEADL951wuQzbFAY8Pa96v3/7OH68daRP+xf4hg2HTnosI/L+Xwd4/y8tgeqOp6b63idx6/cNv+ebK12f3zkLLvm8afYITguE4PEhX904jE9XHsRuV7GrKnalZrPaFdq3iuDbdfX/ohnliLR66rcd1UsGVctQEo5jWTuWq8/VLC1obbXjUrON3zYfdTvWvLvH0DU52rf/CIJmwdBOCdw/NYPDBRWY9DIGnYTeUUxWliTt1bHEqigqVrv2ObYpKnZFwWpX+Wh5ltv+NxwqpOMDs6qPRRLC0OGXTTn1Xu/ZNgZV1b5HLhnczj8BGBe8D1OfB5sZXu3VuD6SekBKH5j4mG9tE7RYhODxIcM6J3jModM1OYrnZu90eS3KpKfCakdRVVQVVFVFUbUaWYrjuKDM4rPSFDd8spZF9433SV+C5oVOlrh1XOOL0gKUmW0eBXwVGw8VCsETIjx+dk9uGtOFj1dkuYzUunFMZ2b404eniqgk7fWR43AyC365Aw6v9P7+5F5w3jt+MU3QMhGCJ8DcOKYLN47pUm+bSqtdmxlSVZSqWSJVRVHgqg9W+0TwRIfp+e6WEU3uR3D68tKFfXnpwr5O5z5cdoAnf63rD3Tb+KaJK4FviTDpWLU/3+W1O7/aiMWmcOGgACXzs1WCuRg6jWmY4MlzXfFdIHCHEDwhxI8bjnD315sCMtb9UzOJMumx2BSnKDCBoDGsP3SS89z4hXx4zWCR4ynE8JR1Wa8L0HfBnnmNr5ZedEgLcZdFQkuBdwjBEwCKKqzkFFZgs6uUWWyYbQqVVjuVVjtmq0KlTdv/cUP9a+u+5NGftvLoT1s9ttv+1BQijOJjInBmT24JRworsNtVbIrChsOFbtv+tuko4zOSAmecwC0llVZ6exA7r17cj5n+SlNxYi+8OdBzO2+oLIJlr8Loe3zTn6DFI55kAaDvk42rYQPQJTGyulREddkIGcrNdnAUAVQcDtJV+96kjveGKT2TMerErycBPDd7B+82svxJRICzjgvcs+7gSY9tBnX0Y1HiWY0TJ7Y2A9HrZFAVbasshtQBMOhaHxsoaMkIweNnPlx2oMH3pMaFc3bftg6BoyUIkyWgKgrLcSw7imXViCHtmiRRHbFlMZuRgPCI8Ooor9825zCgfTwDO8azL6+Uid2TiQnTOyVFlEUhLkEtPlqW1aj7UmLC+GL1IXq0jeFvQ3xdOFLQUDJTPOfjGv3iQm4e24X7p2T4fpn7b1/CgSVgjILF/4Kspd7dJ8twZE3NsSESRt4F4XG+tU/QohGCx8+M6ZZIdJiekkqb58YOcosr+WDZAVS1ZtZGRZvN8RXL99U4LD4zy7Xz3xc3DMVsVbApKn3SYkmOEaUBTld2PzsNgLcW7uWlubu8vu9YcSUAD/6whd6psfRKjfWLfQLP5BRWMPOtZR7bqaqWfPC+MzJ8n8jYGAkZ2meJlF6w7HX46xWPt+mz1zifsJbB4VVaHwKBlwjB42e6JEax5Ykp9bax2RUe+nELP23MQefIgaLTaaUi8sssAAzvnIAkadFVCVEm7HaVr9ce9qvtl763yum4qhK84PTltvHpLiOuVuzL52/vuY+wSY0Lp6coMRFUNh8p9Bjh+c+pmVw6tD1RJr3/Z3nD42HS49p2dDO8M9q7+2b+F/pcrJWWEAgagBA8QWJ7TjF3frUBu6JytKiSCmv9RUZXuAkhbSyZKdFagjmdpNVG0orrVGdrBy3xWJhBh0EncdmwDj4dX9D8ySms4O9fbqDcYmf7UfclJwCW3j9eRAEGGYu9/iniCwamcd2oThj1fvLbW/AsLHmxUbdau07HEJ0AHUdDn4t8bJjgdEEIniDx5sI97MkrDdr4O4+V1Dk3rHMr9I7CkAadVqhUr5MpLLdwzYfalPLfhrTn+fN6B9pcQQjy7dojrPXCCRbg05UHuWpER/8aJKiXRTvz6r3+3bojfLfuCB9eM9g/UXX7FzX6VsMeR9bu9Z/ADzfUXLh5mVjWEniNEDxB4rVL+nPnxDJ0suZIrKhgttk5mF/OrZ+vd2qbkRyNxa5gsSmYbXbMNgWzTTv2JSv3u69YXMWXqw/x9IyeWsSE4LTm7xPTmdIrGatNRXJEDNoU7bN5ybvOy1tn9WkTJCsFVTx/fm8uHNSu3qVHgB5t/LD0qChw1S9QlA0F++CLWrM07UfAofpre7nF4OPCpoIWjRA8fmDJ7uMeCzNGGnWYDDoijDrMNoWSSiuVVtcCZlduCW1iw9DJEtFhBuIdMy8GnUSYXkdUmB6TXous0sua74/seFXsVmRJIjzMVL2EJUsSOpnqdlV1k3SSNqtTtcmO43KLnR83HMFiU7h/SqYQOy2cOVuPcfNn6+ptkxBppEtSlJYJ3JER3KaolFvsHDhRVqe9ySD8LYLJ3G3HuOlT1+/pdaM6cfWIjrSOMlXXVPMJZSfgpfqzygONFzvnvAkJXvQvEDgQgscP/Lkj12ObMoudMoudgrrPhmqSok10TIhkeJcEJxEiSzgJkhqxQs05h5ixVFYgSxJRkRFObfU6iWiTAaNexugoHml0hKVHhxnQneKweN2oTk39ZxE0E3bn1l3uPJX8Mgv5BzzPCAKM7tqa79Ye5uqR4jMULDokRLi9VrsyOsD+5870jcOyKRo6jYUDi11fN0ZBTFs4sbtx/f9yOwy4ovH2CU47hODxA0+c3ZOOCZF8s/Zwk4p95pWYySsxszrLuweLrxHV1E9P/j6xK8O7JHDh2yt80t/SPSdYuucE03q3EakNgkRmSgwvnNebB37Y4rHtjmPF9Gzrg/QBepO2jFVFeYE246M6ZrItpY0XOwJBIxCCxw+UWmw89VvdAooNJcqkx6iX0ddadjo1F49aK0ePiqPKOjU5exRFQaWmTlZVe7tj+cGuuI7cyEiOJkk8nE5bFu2q38HVW64e0ZEdR4vp3z6epGiTT/oUNA67F4m8zuuf6lVywkaRva5G7DSG3heBJGvho4dXwbTGRXwJTl+E4PEDMWEG1jw8idziSsw2O5VWhezCCu7/bnOD+ik128DF5NDqhyZ6LUaKi7Vw4ZgYkQNF4D33TcnknskZ7DhaTFGFlZgwAzZFS0L5zG/b2XSkyKt+erSJ4YlzevrZWoE3XDa0A5cN7cCuYyVMeXWJyzaVNnud5WyfoWtiAdnz3/ONHYLTFiF4fEx9VaN9xfQ3/mLNw5P8Oobg9GJrdhHfrTtSU6/NUZ5EkuDA8TL+2K75pQ3vnOC12AEoKLf4y2RBI+nUOtLttT5pcf4buPNYeCgH/r+9+45vql4fOP45J0n3gDIKZe/VlrIVUIaCLMF1ARUH/gTR67jq9YrjXreCer0/97xX5Yqg/lyAgoCCgDJkFZQ9yqZQSkt3k5zz++MkaUNX2iRNG5736xWbnHNy8k0tJ0++43meT6j2U/Ou+YSKWy2EZyTg8TGL6vkKh/E9EzCbKvg2VU7vs3PT9EvbV79hQlRi3OtrPDquvASYCbFh9G0bx13DOvhvOET4jMWk8OK1ySzcdpzVezPc9s1avIsZQ/y48skSAWP/Cbu+A9UCqhlMZji4GgoqnqsY+dUU+Mr5SIEns/zXRhG0JODxsaSWsfzl8k787/K9VR67IPW42+M1Dw8jIsRsJPxTz6uQrpQUCZWMtcLXfnxwCD/vPs2+07l8uv5wtZ57PLuQBanHy/w9l7bqoWG0rmSlkPC/X/dnlCkXU6vO7IfXe3t/nsF/8f4c4oIkAY8f/OXyzvzl8s4AtJ35ncfPGzx7hb+aVK6nxveQ7LcCMGq+dWgSha7rdG4axebDWUbRWt2Y4P7zntNVlj+pSGSIkStKBE5leXhKC/VXWYnU+fD1Hd6d4861EN/dN+0RFyS5CvnJf9em8fdv/wh0MyrVPFZWYYkSmXnFTHhzDUcyC7w+V+OoUN69qTd92sT5oGXCW5EhVV/qxyQ1K7cwbI0dXAUfX+m78719sfvj23+Cln18d34R9CTg8bHXftzLK8tqlltieNem3D28IxZVLRnKciQTVDCGslTXEJdjuEs19kHJHB/d8c08JsyCYjM+vGSVlqjKiewCr4Ode4Z3pFVcBEU2DWsVxSpF7RncqTFps8YCcPRsfrm9yd9vP0lWvpX3b+5LZKgPPhqimnl/jsp8MNz42bgzTP8ZQmTIVFROAh4fy8yr+aqUn3ad4idHgb89z472ybydc+e8/7YuLgw9EmJJmzWWNXszmPLvms31eP2nfW6PF9w9yL8rf0S1jX2t4gnqv+4/w/7Tub75f9akMzxZyYq+cyfgzD74eJx3r5OxB+zFgAQ8onIS8PjYk+N78OT4Hui6TqFVo9Bqx67r2B21hpw1h+yaxtGzBdzqqEJ+vs6PL/ZL+96/uS8jusf75dwiOAzu1Ji9z43GrpX83Ro/NTQNNqRlcu+8LR6da/wbvzD72iQm9Wvt51YLT31910A+XX+YD0qVk3Dq3zaOzrWVXT2muXGLaAz5GVUf73TFC3DxXf5rlwhaEvD4iaIohIeYCA+puGhix6bR/PjgEDalneXRr7czpHMTftp9iqgQMy3jIrDaNfadyvVpu6bN2cjyB4bQsWmUT88rgovFpFJRvc/xPRMY37NsLhWrXeNUThGDZv3ktv3hL7cTG25hVKJUTK8L2jeJwlLB5OSBHRsRVtuFXqd8Ce8N8fz41f+EXjdCmA/KX4gLigQ8AeZcGWPTdH50DGflFNnYeeKc314zopIgTIia0HWdS2av4OS5wnL3927TsJZbJM6n6zrFdo2CYjtvr9xf7jGXdGrirxcHuxVshWArAnsRpP0CX0+v/rk6DDPy+QhRTRLw+EFmXjEDnl9O5/homkSHUmTVsNo1iu0axTbjvtWuu35m5JZfXNSsKiy8ZzAx4RYsjmrpJkc9rZjwshXNyyOlJQRARm4xf/r3Zs7kWb0+V2SICbuuo+mgaTp2xyT5inzz50E0jZYVgYG04WAmE9+tvBjsWzf25nROIftO5dKhSaTv8n09G28EOl7QQ2NRUq6HftNAVeHcMVBMjtpaKqil7ru2mUvd/LTcXtQrEvD4wZQP1mO16/xx3LteGpumM/rV1T5qlW/Ex4Ty80PDar/bW3jlWFahT4IdgLzi6uXjuerNX/j+3kvoniBBd6C0bxJJ+8aRHMjIq/CYu+ZuLrPNubLLK4Pvh5UveHUKpSgb1r9j3Lw16D4Y8bT35xH1jgQ8fvDgyM78z8cbXY/NqoKtgqrk9U36uSI0D6oui7qlZ8sYtj16iaunz67prl5Hq03j+99P8vdvfvfb64f4K6Gd8EjjqFBmDO1QrQLG0b5Ymg4wdCZ0nwBvXeSb83mrw/BAt0AEiAQ8flA62AEqDHbCLCqNo0KJDbcQG24hKtRMqMVEbqGVFbtPlzn+9et70SgqBJNiDG2pqoLJkZNHVTGGvBT37fn5uaiKQoOYaFRHuQpTqeNV57lKFYwUwc8YHjURZjGRXWD1OthpEGFhUMfGhFtMhFtMaLpObpGNey/rRIcmMkG+LhjUsXGVx7w6OYUJKS18/+LWfN+cp+0lcP08CK2llWQiqEjA4wdrHh7mUZmIQquxNP3o2cpz5fzx1BU1TgR2zmIDICZG5lCI8p3M9m5+BUBWvpXvtp0osz27wMpHU/t7fX7hvRYNwunXtiG/pZ2t8JhVezL8E/C06AN3rYfFDxkZmGsqbTVkHoDmPX3XNnHBkIDHD1o2jODTaQN8VqjvwOk8WsdFGPPwzisk6sy6LL0zoqa6NItm//NjsGmaW76o/afzuPbtX70698rdp/nPmoPcNridj1oraupsXjG7TuZUuL9Fg3DuGtaBo2fzMauqq4ixyeT4qSqEmqs5d0/XYeN/jFpauh2OVV3Pq0Jdx8GlfzWqrJ/e7Zi0rDgmLJtKTV42GT9NIRASaRwjBBLw+M3F7Rvx+vW92HcqF5umuSUd1DQdVVUotGpsOpTJnvTKc+1c+UbFmVFr26e3D2CgB13jon5xDnGV1qdNCF/MuJg96TmlEma6J9G06zo2u86yHensTi//w3TtgTMS8NQB76zaT06hrcL9x7IKuOyfP1d6jn+M6169/5eaHb57wPPjK7NrkXGrjkF/gRFP+eb1Rb0nAY+fKIrCmKTmfLr+EJl5ViJCSnpinHNmNF2nVVw4J7IK+e+6Q4FuskeiwyyBboKoRf3axtGvrVEAtNBq559Ld3M234rZkR5Bx1iS3jg6hN3p5Z9j2Y50nl64g39cKZWuA+mhkV1YtSejxjm+GkWGMKxr0+o9yWSGB3ZB1iGj56U4B/57dY1ev9pMoZB4Te28lqgXJODxo2U70v1SMb1BhIVP/mcAseEW17BWmZ8YP/Nyc0BRiI2JrvQ4b+p1iQvDgtTjvL+6bDkCTwzq2MjHrRHVZTapvH1jb4a+vLLMvn9N6snVvVr654WdJSQAzh2v3nNv/pZzjXqBokguMeE1CXj86Ioe8fzzTz05mJFHsV3jYEYey3ZU8DW4GrLyrYx7veww18K7B5PU0j3duslu9MhIz4zw1p/6tCQuIoTMvGJsmm4EyRhTJHaeyOGjX9PKfd4//9STy7pJ/ba6YG8FpWru/yyV+z9LZWiXJv6dZB6TUFJQ9PB6+M/Iyo+fMwG3MOe+VGjY1k+NE8FOAh4/UhSFa/sY35qmz9nok2CnMtWZ63NJp8Z8PLU/qgfZmoUA4+/58vMKz246lMmXm49ht1ecm2lB6nEmpCRgNkkunkCyazrT5mys9JgxtVHv7NhmmDMBiqo5tNY8BSKkp1DUnAQ8teSuYR3ZefIcVpteYb2h2rR6bwZ2XUdFAh5Rc9e+XXm5AoCf95zmgc9Tee36XrXQIlERk6rw5Z0DK1x599n0ixjQvhYCit3fVx3sjPsX9L0NkPI4wnck4KklKa0asPpvRobPrUey2HDwDM9/v8uvr/nJ/wygsCAfVYGoqChMqvEt3Zl8cPfJnFKJB419zvvO7SbVmN/jTGSo6TqqotAwMsSvbRd1374KhkfKc9ewDn5sifBUn0qKuE56bx0fTe3H5kNnMZtU7hne0ffz+l7pbtTBqsqi+42l7J1HEWLT0E0hEBljTEQ2hxhLzk2hxqRoRQUUMFmMbc2SjGOEOI8EPAGQ0qoBKa0aMP3SDgya9RPHsipPPFhTU/7tmzxA5Xn+6iRuGNDab+cXdV92QbFHxyW2iKFrM/l2XhdY7Vql+2/98DfX/S2Hz/L+zX19OxTZYThs+a9nxx5ZD0fWU+2Uqd2uhEmfVPdZ4gIgAU+A/fzQUPKK7SgK6Bpouo6mG7lOih3V1D/77Qjv/Lw/0E0FoGGEhcZRoQzr2iTQTREB1qdNHGmzxtJ25neVHvf7sXO0nfkdX945sNIeBuF/CkYA+vuxqufPrNh9mo6PLWbDY5f5rtr9hDeMm6ZB/hnI2A0/z4YOl8HyJ3zzGjsXGr1DPSf75nwiaPg14NmwYQNbt27l1KlTJCYmctVVVwGwbds2Fi0qSSCl6zo2m41p06aRkJDAypUrWb16NSZTSSK0O++8k4YNjYvlyZMnWbBgAadPn6ZJkyaMHz+eZs2a+fOt+MTZvGJG/GsVGblFgW5KjfxtVBfuGtox0M0QAVRotfPqj3tZsPU4YRbjm3/bRhGknam6VtLpnPr5dx9M8orsHgU7pX3gSEVgsxtfxsYmN3flZqq23FPwcqey270pN1Ge3/4N+1cAupHtGd34Rqk7fp7eDZoVouLhiucgQeaXVSZYPsv9GvBER0dz6aWXsm/fPmy2kgyfycnJJCcnux5v3bqVVatW0bx5yQqBHj16cM01ZZNG2e125s+fz4ABA+jXrx+bNm1i/vz53HPPPW6/1Lrom63H6k2wU2sTGEW9cvRsAW+vrFlv48jusjQ9kM4VWun59NJqP++9VQfcHn/0axpps8bWrBEhkdAsGU56XrW9Ro5uMG5VObMPFj0A06uufXghC5bPcr8GPN26dQPg+PHjrpn25UlNTSU5OdmjCXJpaWlomsZFF12EoigMGDCAX3/9lYMHD9KxY2B7HzLzitly+CzhFhNhISbMqoKmG8NUuq7TIyGWB0d0ptiuuU0KBtzS9X++8Qinavht2GJSHBOTjYnGzoSCZtcE5NL73Ccph5hVQswqiS1iSWndwIe/GVGf2DWdr7cco9Bqp3FUKCFmBatdx2rXsNl1/j6uOyezC8jILSa/2EaBVaOw2E6B1U5+sY39p/PczhcTZuauYR2lpFEAaZrO6P9dXeH++JhQhneNJ9xiwmxSXHW0zKrq9hhgcCcvSstseM/zYKfdpYCCzW4HwGwyGT0yrfo7JirjqJOllPqpum9zTmh226ZAfiZENoaIxkZhU1GpYPksD/gcnqysLA4dOsT48ePdtu/Zs4fZs2cTHR1Nv3796NevHwCnTp0iPj7e7RcaHx/PqVOnAh7wDHlpRaW1ampDbLiRYPDZqxIZldhclnSKCv26L4NF209gUhRO5RTywx++zxO17cmRxEjSy4CzaXqliyPSzxVxMCOX+dMv9m9DzlSjd9AxzFXmQ2r75yX3B9wJo2d53SzhvfrwWR7wgCc1NZXWrVu7xvTA6ALr06cPkZGRHDt2jM8//5ywsDCSkpIoLi4mNDTU7RxhYWEUF3u2YsQfNE1nzto0GkaEBDzgycg1fg9PL9zBqNpIIibqrRs+8N8qvvE9EyTvTh0SYlY58PwYnl60o8KM2OsOZGK1a1j8mSDSOWn5synG5GJvrX8bUj8Fuw1u/BzaDvb+nEFi06ZNbNpkVKfPz696jp236sNnecADnm3btjF4sPsfaZMmJSuAWrVqxYABA9i5cydJSUmEhIRQVOQ+3FNUVERISODyLuxOz+HJhTt8ft5WceHERYSgqkbeHOcQmLP4qHMoSil1X1WNn3GRITyzaAdWazGqohAWGuqon1XyHGcdLVXBlXHZ/byw5XAWO0+cc1XKLrZrPHFld3q1bugaJnMOnVlMKpGhAf+TEh5a/sAQ1h0446qAvnLPaVbtOe2Tcy9IPc6C1OMsumcwiS1iq36C8DtVVWjTKKLSYzo9ttjt8auTU5iQ0sL3jQmJ9t25Ch2lKj4aW1K2QtCnTx/69DGG69577z2/v159+CwP6KfT4cOHycnJoXv3qqso67qRur5p06asXbsWXdddXWHp6emubrJA6NY8hv+bcTGHzuSj6TpmkzMgccybAddcHmM+jzFPwrn8fOZX28s9r6ZBTqENHVzPK++nphu/H+dxmlZ6n+M+jmN0sDt+1tTdn27x+FifLmkVPtWxaRQdm0a5Ht82uB3ZBVYKrXaKbRpFNjuFVo1iu0aR1XhcZNO447+bPH4NZ823xlEhrPrbMCJCJCAOpKmD2jF1UDsARrzyc4W1tZzum7/VPwHP1W8bt9IKs+H1PpDnRdA9ZwLc/K13bRPVVl8+y/169dE0zXVzLldTVRVVNbpMU1NT6datW5lurV27dtGmTRvCwsI4fvw4GzZsYPhwI0tx27ZtUVWV9evX07dvXzZv3gxAu3bt/PlWqtS3bRx9a7hUc3L/miXwO5NbxN+//Z1im+YKsFQVFIyqjgpgs1pBUfh5byYFtsqTjvmFF4GVqF2aphMZYiLMoqI5ckLZHQG0VipIn31tEou2nQBwC6w1zQimd5/MIbfIfWi3T5uGhFvq9irKYGfXdD76NY1nFlXdGx1mUbkyOYH7R3QmI7fI6GE2KYSYVML89f9x7VveBTsAx7caFdkV9bybc0KzqZx9aslkZlGuYPks92vAs2rVKn7++WfX423btjFkyBCGDh2KzWbjjz/+YOLEiWWe98cff7BgwQJsNhsxMTEMGjSIlJQUAEwmE5MmTWLhwoX8+OOPNG7cmEmTJtX5Jen+8MGag3y//WRAXvvxsd0Y1LEx3ZrLZOi6aMWuU7y36gDnCq0oCmiODLvOfyfOa7uCUem8uIoMvN5696a+fj2/qNpXm496FOwAFFo1vth0lC82HS13/8XtG6Fj9BQ7v9O8dF0ybRpF1qxxWUfgZx9MPi7Mgle6eXeOhu0A3ZgXZM2DKV9Bi97et60eC5bPckXXvRncCB7vvfce06dPdz2uD6ub7JrOqr2nsdo0x/CYsb30hejeeZ4PP1XXC9ckcXWvFv77xnee+vD/BMr+LQXCgOeXk36u7uR8qmwuSF3+/xrotnnzt3R+2+2azrwNh9l3KpeWDcN59rudPmsnwDW9WvDKpJSan2DnQvj9K/jjK5+1ySf6TYOxL3t9mkD+LdWFa1JdIAPq9YBzPkWRTeNwZj7XvFV+teNre7c0vs1rOl9t8aBAn5ce+Wo7G9PO8s+JPf3+WqJ6lj0whMOO7Me6Drl5xlyNiAj3b+Al88Pch62cQ1V2zX2umPFYd81Ju8eDgLpnqwYM6uhF7hbhEyZVYcpFbSi02pm12DeFiz+dNgAFY9GC12VD4hNh9+Kqj/OnLmNh5DPGfdUModEQLuVQgoUEPH6m6zpWu06hzU5hsZ3VezNo2TDcNRH09+PGqoJwi4lCq0aB1c7C1OM1Kij65eajJMSGuVZcVaZFg3CGdmniWvGlKO4rtFRVIa/Ixq4TOZjUkqSEIWaVEJOK6khm+NCoLtVup/C/mDCL2+qoc+eMvwlff7u8tFMTvtt+AlWB3CJbmV6D1CdGunJDicArstnp+vcl5e67Z3hHQkwqoRbVlXDQ+e+89EpRi8nY16dNQxpHhZZ7rmp7soKVfLGtKW43HF0xERoWbgQhqsn4aXP0YLrN0XEmGiw1L0cxnZeE8Px5PY5l+KYQ6Hm9VFoPYhLw+FCRzU6Xx8u/mHjKYjKy2lbkuasTiQgxoetGMjEAdGgWG8alncsW9Ax0l7wILvnFNt5euZ/Xf9pX5bHX9WkpwU4dY1YrzrHz8a9pfHBLP/q3q2GdLG+oZtDKyWGWfZiQrR8Z9/veBr1vgYSU2myZCCIS8PjQ0ioy1T5xZXe+336CWwe2o2lMKKGOHpPoMAsNwi2EWUyuUhNC1EWv/biPd34uP1vupL6t2HnyHI+N6SZ12Oook6qQNmssdk3nhvfXsf5gpmvfuUIbd83dxMbHR9ROY6qbfHDjf4yb5NoRNSQBjw+NS25OTLiF7AIra/dnMG/DEbf9TzmSE/6WdrZa5/37uO50bx5j9MBi1MYquQ+guO1z0nWdvDyjrlFklr3C85fuTzKmsOuu+859CtC7dUOPhstE8PgtLZM/vbPWo2MHd2rMLQPbouk6qUeysOs6CpDSqoFHtXVE7TGpCjuOl62JlJFbzK0fbiDc8eVrRPd4/+ThgZplWo5oDJ/fXKoCurOXWyvZ1rIv9J8OYZLwUriTgMeHFEVhSKlhpfMDnprydCmpv90+uB2Pj6s6sZQIHst3el5fq6IJzPdf3pn7Lu/kqyYJL+i6TpFNY/wba8gpKr8MzsrdJblwFm07weXd4n2fQV3X4fHTUJwLL1Yj70p+BuyoIrHgniUQ2xp6TvKujSLoSMDjJ1cmN6drs2hyCq2AwonsgmplKPZGYosYTIqzCrt7/pWKKBi9Oc5eI+f3cWcJCrNJ4bbBgU3uKGrfI6O78eCILny//QQhZpWEBuGuUhR2TedARi6Pff17pee4fkCrWmqtqMyn6w/z6NflZ3V3GtqlCZP7tcJqN1brdWse4/tg5+1BkF7530yFrvtP+RXQS1dHD4uF1n4ugirqJQl4/ERRFDrHR5ORW8TJ7MJazTL7fzMGunLjyKRl4a0Qs8pVvcof1vhiY+W9mLcPbielReqInq1iiXUMuVdk5e7TvDqpF7ERfpxs3usmWPJw9Z/XoDUkXuv79ogLhgQ8fvTK0t285sFqlprq3boBfx7WkYgQMxe1j5N5EqLWvXBtEm0bR/LKsj3l7p/YT3p36oruzWN4/+a+PP7NdvakV1xDy6/BDkD3CRDV1MiKvOh+j56Sc9saolsn+bddIuhJwONHpVdA+MPmw1n8z8cbXY9HJzZDdYxHOXPq2KxWFEUhNCTErQq6W4V1pWQitPPxmdzicpMX/mtST1rHRaIquHL4xMeE0STaR/k4RL1yJDO/wmAHYOS/VgEw65qkGteME76xaNuJKhNFTkhJ8H9D3hsCuZ7PDQOI/o97FW763gYD74U4GWYXnpOAx48+u8MYRy602jmdU4SqKtjsGkU2I+lggdXOe6v2s3znKZ+83uLf/V9X6/7PUsvdLgnmLkyhZs+Gamd+tZ2ZXxnzR767dzA9EmQFTW0b0T2+ymO+3Xqcb7cedz1eev+ldI6P9m1DrnwNNn8MZ9PgVA0XZDiXqJcnvCH8ZbuRJVmIUiTg8YNim8Zdczf5LJCpa9o1jiQmzOwqNTAqsRnRvp7YKALOrulc986vbDmc5bNzWkyK77Lzimo5m19c7efsSc/xPuBJWwMfjfXuHNVRcBbsFc9TEhcu+ZTyg42HMqsV7JhUhS9mXExMmBkoGWJSFVx1apRSw1GqY+WUct7jkuGskufn5OSgKgoxMdEoKGWGtYSoiK7rXgU7mx6/nEYS3NQZzWPDWfHXoRw6k8f9n23lbH7lQcGeZ0cTYq44M7PHQqK8P0d52g+Dm7/xz7lFUJKAxw+yq7iQnM+u6WUKgj4wojP3XuZ97hKbY7WWp0MPQjiZTSpps8byyrI9vPbj3mo//91VB3h0TDc/tEzUVNtGESz942SVwQ4Y1yWfSEgpmx05+xh8OBqyDnl+npHPwcC7fdMmcUGSgMcPRic1Z1LfVnxWxZLdymw7KunTRd1w7/COtG8cSYHVjq7D3lM5fPhLWpXPmyQrtOqc1KPZvFBFpfR1j1xGs1g/pxL4V/USmNpaXoz5orv81BhxoZCAx0+OZuVX+zlps2pxnFsID5lN7nl4jmcVVBnwXNOrBSt2naJlw3DpXaxDOsdXPbyUXWD1f8Bzw+fw6USPD9dDIqGSwqdCeEICHj+Ze/tFAGiazlML/+DjtVV33c78chsmVXEt966I4pjbc/42133HY12H4uJiUCA0JKTUscbBY5Oa07NVA8/flBBAQoNwt+B80bbjZbKIf7XlGF9tOUaXZtFc0qnJ+acQAWJSFSJDTOQVl19bb8lfLqFLs1pY3dT5CrjkQVj9z8qPe2g/5+zGtUvWgApvScDjZ6qqMKFXCz5ZfxhVAbOqoqNTaNVoFRfOkcwC17Hzf/NN7S1PvbfqgPQqCa/ouk50mMUVYJd268C2EuzUMftP5ZUb7IRZVG4Z2JZOTWtpKbeuQ2QVfxvth4JqBpvu/o1OiBqSgKcWpLRswMOjuvD897uw2ksuNqWDnYr0SIjhyp4JpZIEKq4K5vr5nzA4K5zr6I5rRGFhEQBhYaGu/U7Dujat+ZsSAiP3011zN5e7b+borrXcGlGV7gnll5gptGqMS0rApNZSYJF1GJbMrPyYAythdhtC+86g6NLHaqVZIrhJwFML0s7k8fz3lU8UPN8dQ9oTHx3GrQPbonpxEZJaWsJfim0aA9rF8ac+Lfli01G3fT/85VJXPTdRt6TNGsvhM/lMem8tJ7ILXdtbNgyvnQacPWTkyrnyVTjluC6uf7vscaExoJqwdrumdtolgp4EPH70j29/Z44Hc3dKM5Ky6Xyx8Si6rvPaT3vRddCMrht0oFfrBrw9pQ9RkuxP1KIvNh7hof/b5tGxDf1dj0l45dKXVpTZ1uuZZa77I7vH897NfX3/wod+NZajV8YUCroGRcaXtaj/jix7TIPWcPdGMEueJ+E5+cT0MV3XeXPFPl5eWnF9ocpk5BZVeczqvRkkPvGD27YvZlxMv7ZxNXpNIapy6lyhx8EOwNoDZ5iQUn6FdRF4H97aj6kf/Vbh/vScqq9DNdI8BXpeD6nzKj7G7sFrZx0GrfyJ10JURAIeH1u2I73GwY43nlm0g+7NY8pMHC22FqMoChaz59+4dccsofNXgumUnNzodXJs0+Ga3i0Z3Klxzd+AqNOaRIfy7FWJrN1/hiKbvcpM4vfN34pJVRiXXAvFKEW1FNs0BrSP4+5hHXljxb4y+xNiw/j2z4P88+IhEXD1O0bxz3+P8O5c/70axrwIzXv6pm0i6EnA42NNokNJahHLgdO5FS799IdtR7MDmqzwqy3HZMVXEFMUhSkXtWHKRW3QNJ3/Xb6HTzccwawqnDxXWO5z7v50C6MTm9feRFhRpU/XH+bRr7dXeszfx3XnTG4RJlVxlagxqQohJhWzyUe5cFr1N7Ivaxo83bBm5ziyDv49EqavBEWt+qaaHPcV4ycK6HYIayCrwC4QEvD40KmcQq4+r0TEhaTtzO8AeHVyigxnBDFVVXhgZBceGNkFm11jzGur2ZOeW+a4u4d1lGCnjhncsTHNYsIqDFIB7qxg1V2zmDDWPjLctzX4DpSdS1QttkJ46yLvzjHgThg9y7tziHpBAh4fahodxj//1JMNBzMpstk5ea6Qo2cLOHq2AItJwWr3UW2aOq6JFIwMepqms2ZfBlkFVoZ3jS834ElsERuAlomKZOQWlTtZ2VM3XdzGd8HOp5Nhz2LfnKsmFBOYQoxbyvWBa4eoVRLw+Ni1fVoyJqk53f6xxG27p8FOiElFVUty7iiu/Du4dTGX3u/MzFz6WOOxgq7ZURUFi9kESsmsnPKuW875P/r5G3DfXnKc7v7YsZrsyYV/oCoKb0/pQ7vGkR69b1G/vLNqPy8u2V3pMTM+2US4xcTXfx5I12aSFiHQbNX4wuX34WnHCiyf+UemMWQlRCUk4PGDULNKx6ZR7DtV9ltvVYrtGgTJ4oO56w7x+LjqFQkU9cOxs1UnzQQosNpZu/+MBDx1QLPYMNJmjeX699ax9sCZSo9tO/M7lj8whI5Nq669VSNTvzd+7vgWPr/Z+/PpGiABj6icBDx+oKoKyx8YQlZ+MePf+IXDmdUvJOoLnZpGoesaJkXBYjFhUhRUR2+Qcb+kd0hVFMyqc39JD5LzsQJlsjxrjrxAmqbz3fYTXNa1KQkNwl3n7NA0im+3HmNMUnMsvprsKOqE8T0TmLv+sOtxiNn4/1ts08oc+9TCHVzdqwUNIkLK7BO175mrevDMop38vOd0pcfFhPv54+FsmvfBzjXvQ5cxYJK8T6JqEvD4UYOIEFb9bZjrsa7rtHvk+1p7/b016GHyxo+7yl+qfPRsAX8e1rFW2yL8a0D7RmWGPQqtdvo9t5ycQluZ41OeXsbBF8b4dsKrqLb0c4VY7Tp3XNq+woDn96euqJ2kpuZwCI+Dgsyan2P5k5DsedV1cWGTgKeW/Lovg7UHznDn0A7YNR2bXUfTdWya5nps13XsmnFz1sTSNGNejKbD8p3pgFFfK6/IRmSoGatdo8imcehM7fcijeweT+u4CFRVYWNaJh2bRrlW5ei6cYsOMzN1UNtab5uofWEWE/+amML9n28tN+gRgTXxnbVsSCs/uHhlYk/smk5ii1j/BjtFOfDRWDiRWvWxjmDI1vIirJ3GEB7VACzhRnZluxWsBdB2sP/aKoKOBDx+9su+DKb8e32ZhIDe+ON4yYS/UT2aEWZR3QKe+JhQ/nJ5Z0JMKsVFhSgKREVGlAxTlZro7ByyMqkKoWYToWbVNflYBxITYnyXe0MEvX8t31Mm2GkeG8baRy4LUIuE08ge8RUGPA98nsq2J0cSE+bnoSFTiGO+jQcKMqH3zeQPeRYUhXCpByi8JAGPn/2485RPg53SXrw2mYs7NMKkKjwyppvbPByTaszTyc/LQVUUGsTGyHCC8KsjmfluwbjT8K5NA9Aacb7bL2nP7Ze0Z/6Gw8z8qmzywQ/XpHHf5Z382whzKMxYA1lH4L2hkJ9R+fGb58CQZwC5dgnvScDjZ/+4sjsPjOyMza5hcwxX5RRamfjuOjLzir0699++9Ly20fm2PzmSaH9/mxMXjIJiOwtSj5e776nxPWq5NaI8fxzPZuxrayrcHxHi51VOx7cYQY6nJn8KXcfCOR8vYRcXLAl4/EzXde78ZBOr91bxTaYWjegeT5hFlnAK790zbwsLKwh0wKjLJNmW64ZCa+X5Lp77ficd46MY1sVPPXLFedU7fv4NAFguew5rj0lGsVBnaQghakACHj/LLrAGNNhRcJSOUZzLyxV+3nOapCd/cJvTozqGwBRFwaSWXpaOsYS91HwfXa98BdiOp68gIkT+tILJgdO53P/ZVnRwTajXdcodwirteHYh/1q+lwdGdK6dhooKeZIaYuqHFVdQP19yy1g+m34x4Z72DLUdbNTPctLsUJwLu5fA19MrfFr4j48R/uNjHrfLI60HwuS5peprnX8zOS+cvn1dEVDyqeRnDSJCWPXQMA5n5hurrnSwaxpWuzG8VWzTKLTajXw2unN1ltEzZKx0Ksl349rm+LDRSt3XdR2rXWfF7lNouu6qdG6z29F1HUU1uT1fK3VuzbE6TNN0rI42We06xXat3LwqVZm1eBdPT0j06e9RBNabK/aTWsPitIkJMtm0LkhqEcuzVyWyNz2HuesPY9N0Lu/WlKoq31dk29Fs8optngc851NNEBYLPSdBfA84sRXCG0JxPljzIesQrP5nzc5dlcO/wovtPD/+lkXQ7hL/tEXUGgl4akHrRhG0bhTh99d5euGOgFZMd5qz9hBz1h5y23bDgNY8f3VSgFokakLTdGyakT7hH+O6Mz4lAc3x+Hh2IX//5nePzjOwY2M/t1R4wlnxHuCpUl9InEV/q+vF65L5/Vh2mZ5i5/3SZXCMsji4Has4tqsKqCHtUdq2R1UUI12HXSc66wMa+eSd+0BhNhzZUGpITSnVG1TqfpntJccqefmOx7kVH1vu9vPPLT1PNSUBTx13JDOfN1fsw2JS3f7GjaEq9z96bydB+9OWw1mBboKowLdbj3Hf/K1+O//K3acYl5zgt/OLmtt2NKvGz/3b/9V80UR5IijkGcuHxJOJjoIZjf5mCybd6tPXqZHPbvT6FNE+aEa5ngz8l9z6QgKeOu7BL1LZcNCLTKQB8PCortw5tEOgmyE81Dneb5diAMYkNvfr+UXN+SPQLZ3ny5kmw/nVTHf8xzVET8mcsNH2DVxrWu1+Mj+l9HBjDnNUTzeDagbVUlKIVNcdDdYquO/8iSO/kF7OPuN43bFNcW7zhYv+7JvzXCAk4Knj3r+5L4u2HXerdFz6QuHa5themq5DYVEhAKGhoWWO/+/aQxzL8qwIpCdmXZPE5P6tfXY+UTu6NY8pUyYiM6+Y3s8s8/rcY5Obo8oqrTrr/Zv7cPkrq3x6Tk0HzW582Bedt2/tI8NpHhte/hP1UXDkGrCWXs2lkJefj2XfEkKOrYOMPT5tKwA24xqJJx1JUxdDiz4Yw0vOv2vHT+fwlfP+eT3wOY7l9TGSQDFgJOCpo+yOgpxFVjsWk4prFXmpicqUbDJ+6s7HxmRkm10jMsSEoiiEhJrRHHmAdp7I4bONR7xuo9RGCl5hFpX2TSI5cLqaS4lLmXJRa568UnLwBNq5QivJTy4NdDMAuPiFn9we91DS+C700UqfE+nPBlXXh6Nr/FSvw5zHTxmJG0WNScBTR725Yh+vLPPDt5ka+N9JKZhUYzIhGHOHhnRuIsFOEIsIMfPTg0PL3Xf4TD5DXl5RZQbxT9YdpkdCLNdLr19AhZhUWsWFcyTTd725vpLt63CmaXdo1AHOHYe8DGOlV2xrYwhJs5XcCrN8+7r+1uMaY7hNeEV+g3XAtqNZzPjvJk6cK3TlvCm2+2iM1wc6xUfRIyE20M0QdUTrRhEcfMF9CGz5jnRun7PRbdvfRnXhuj4ta7NpohxhFhOr/zbc9Tj1SBafrDvEF5uOVvicRfcMJrFFLfyb1+xQfA3kpsOcCXDumHfnmzgHGvu5PEYNnZMhrYCTgKcO+GXfGY5nG+PINudEtzrk1lLJyMp+q9fL3Vd6TpFrYqKuc+NFbfjbFV2kd6ge0nWdeRuOcDAjl35t47BpOla7hs2u8/32E2WOf3HJbl77cS+bHh9BpD8rcAuPaJpO+0e/9+jY//xykKhQM7oOdl3HbtexO3t4yzlecSQ1db2WrhMbbuHBkV0qztMzuy0UnK3muyhHo04QEgGNOsIv/1tBIsHq3BzLv11JCUslJ1RLHVd6cvKZ/bBtPphCwRwCSX+CoY+UTH4WdYJcheqAO4d2YOqgthRa7VjtOjbN+BCx2o0EgFa7xoaDmTy9aIdH52vRIJwm0aFsO5qF5oPY6XTO+VMPa+7tlft5cERnzCYJeOqbl5fu5s0V+wF4f/VBj55TaNXYeeIcfdvG+bNpwgOKAhNSEvh2a0kpkEaRIRTbNHKK3Cvcf7XZy54Whw/WlP07sZgUfn5oGAkX/RlWPFv2Sc1TjKrq6KDZsdutoOuYFMWx4qn0zQ7ZjvmIJ383HtcFq14ybqWU268zYw00k/xktUUCnjoizGKqtL5VYotYbhtcjcygQE6hlSnvryX1WE6N2tSzZSwWk4rZpGAxqYSaTVzXpyUdm0Y6koqVJBYrL6mYc5uzvAVAeIgJswcp7kXdM7lfa1fAE2ZRXdnCq7Iw9bgEPHWAoii8OrkXr07u5ba9oNjOgOeXc67QVsEzfctq1zl5rpCEIQ/BkIcqP1jXKdy2ALUgk/BGLcFaYKyqKs6FRffXSnv9KjcdkICntkjAE8Siwyz895YUdF0nZdYatyGn6DAzbRtFOiq4l1Ryd95sms6RswXY7JqxTdcptGq0jotgVGKzwL0pETCt4iLKLF8vLbvASs+nyq4GunNoR382S3hI13VW783g5v9scG27okc8P/yRXunzHh/bjU2HzjJjSAfCLCZMqpFfx6QqmE2On6rqvl11Zlf2oCd3x7fw+c3G/ZQbISQSso/BbiMDdI2nNV8/Hxp3Pi8z8vnZjMvbR8XHlvfYw+F5mcMTeBLwXAAURSkzybQmkp/8wbVSSwinQqudH/44idWuM3VQWz78Jc1t/2s/7eW5qxJl3laAvLp8L/9aXv6Kz4qCnTm39WdA+zhCzUav8+2+LiN17gS80rXs9q1zK35O0kSjnlXrgWAJMwKjsAZSZkF4TAIeIUS15RRa2ZOeg6IoPP717+w4UXHV9E/XH+bT9Ye5bVA7/nFl91pspQAqDHZKG5vcnDdv6F0LrXHIOV71Mefb/rlxu2MVNJZeQ1F9EvAIIart0hdXcDa/ejWO/vPLQVrFhTN1UPXmognv7H1uNJl5xew8cc5txaVT12bR3Du8FpdynzsB7w+v/Ji4DtC8J+gaVqsV0LGYVEhIgXiZ8yJqRgIeIUS1vXljb176YbejDhKOWkEGXYftx8ovaNgkWjLF1jaLSSU+JgzzeSU+runVgmeuSqz9lAFhsRCfCOm/G49HzYKQKNi3HEa/CNHxbocXOOa+WGTui/CSBDxCiGob2KExX9/VuNx9uq7T7hH3fC/f3TuY7s1jZB5PAJ3JK3bdX/23YbSKiwhMQ0IiYPrP8Ewj6DcNLrrT2N77psC0R1wwZH2wEMJnftqVXibYARj72hqm/Hs9y3ZUviJI+I8z1Hzt+l6BC3ZcHP2B5/XmCOFPEvAIIXzmZHbFSSp/2XeGaeeVnxC1T4rXiwuVDGkJIXzmkk7uw1yzr00iOsxilCnAmCArhBCBIAFPkLFrOjtPnKPYrqHrOrm5eWg6hEdYXfWsNN2odaPj+KnraBrujx0lYjRdd2yjThU0FXVTq7gIru/finkbjHT/k/pJpXQhRN0gAU+Q+XTDYf7+ze9+O3+jyBC/nVsEh8u6xjNvwxHuvaxuVq0WQlyYFF2vOHXuJ598Qn5+fm22J2BOnDhB8+bN3bZlZGTQuHH5K1Hqk2B5H1A/3kt5f0t1QV3+3Unbyuft31Jd/r1Wh7wP79TVa5I/REREMGXKlHL3VRrwXOj69u3Lxo31f5JlsLwPCK73Utvq8u9O2uYf9bntpcn7EL4gq7SEEEIIEfQk4BFCCCFE0JOApxLTp08PdBN8IljeBwTXe6ltdfl3J23zj/rc9tLkfQhfkDk8QgghhAh60sMjhBBCiKAnAY8QQgghgp4kHnTYtWsX3377LceOHQOgRYsWjB8/nm7dugW4ZULUzK5duzh27BgDBgwgKirKtX3JkiWMGjUqgC2DDRs2oCgK/fr1Y8eOHSxZsoSuXbsyZsyYgLbrfDfffDNz5swJdDOEED4gc3iA2bNnM2/ePCZPnkzLli0BOHr0KPPnz2fy5MnMnDkzwC28sKWnp7sFovHxUmG5Kq+99hpvvvkm3bp1Y+vWrbz66qtMmDABgN69e7N58+aAte2pp55i8eLF2Gw2RowYwfr16xk2bBjLli3jiiuu4LHHHgtIu8aPH+/2WNd1VqxYwfDhwwFYsGBBIJolhPARCXiAzp0788cff2CxWNy2FxcX06NHD/bu3RugltVcMAQJW7duZcaMGWRnZ9OiRQvACEQbNGjAW2+9Re/evQPcwrorKSmJtWvXEhUVRVpaGtdddx033XQT9913H7169WLLli0BbdvWrVspKiqiWbNmHD16lJiYGAoKChgwYADbtm0LSLt69+5N9+7duf3221EUBV3Xuf7665k/fz4AQ4YMCUi7PJWdnc0LL7zAN998w6lTp1AUhaZNmzJhwgRmzpxJgwYNAt3EagmGa5iu62zYsMHtffTv3x9FkZL1gSBDWoCqqhw/fpw2bdq4bT9x4gSqWr+mOQVTkHDrrbfy7rvvMmDAALft69atY+rUqaSmpgaoZXWfpmmuYay2bduycuVKrrvuOg4dOkSgv+OYzWZMJhMRERF06NCBmJgYAMLDwwP6723jxo28+uqrPPfcc7z00kukpKQQHh5e5wMdp4kTJzJ8+HBWrlxJs2bNADh58iQff/wxEydOZOnSpQFuoWeC5Rq2dOlS7rrrLjp16uT2Pvbt28dbb73FyJEjA9zCC5Au9MWLF+sdOnTQR40apU+bNk2fNm2afsUVV+gdOnTQFy9eHOjmVUvPnj31devWldm+du1aPTk5OQAtqrmOHTtWuK9Dhw612JL6Z9iwYfqWLVvctlmtVv2mm27SVVUNTKMc+vfvr+fl5em6rut2u921PSsrS+/Vq1egmuVy5MgR/brrrtP//Oc/661atQp0czzWuXPnGu2ra4LlGta1a1f94MGDZbYfOHBA79q1a+03SOjSwwOMGjWKPXv2lOl67NevHyaTKcCtq568vLwyPSIAF110EXl5eQFoUc2NHj2asWPHcvPNN9OqVSsAjhw5wpw5cwI+6baumzNnDmaz+z9vs9nMnDlzuOOOOwLUKsOqVasIDQ0FcOvRsVqtfPzxx4FqlkvLli354osv+O6771y9T/VBmzZtePHFF7nllltcwz/p6el89NFHrn8/9UGwXMNsNptrTmhpLVq0wGq1BqBFQubwBJl7772X/fv3lxsktGvXjjfeeCPALayexYsXl7t6rq6t5hEi0M6ePcusWbP49ttvSU9PR1EU4uPjGT9+PA8//DBxcXGBbqJHguUa9sILL/D5558zefJkt/cxf/58Jk6cyCOPPBLgFl54JOAJQhIkCCFWr17Nhg0bSEpKqnfzRYLlGrZz585y30f37t0D3LILkwQ8os5yrjop/Y21Pq86EcKf+vfvz4YNGwD44IMPePPNN7nqqqtYunQpV155paTXEBe8+rUESVQpOzubmTNn0q1bN+Li4mjUqBHdunVj5syZZGVlBbp51TJx4kQaNmzIihUryMzM5MyZM6xYsYIGDRowceLEQDdPVGLdunVMmzbNq3McOnSI3r17k5KSQo8ePXjnnXd81LrgVHpeyLvvvsvSpUt54oknWLp0KXPnzg1gy6onWK5hS5Yscd3Pzs7m9ttvJzk5mRtuuIH09PQAtuzCJQFPkAmmICEtLY2HH37YtcQWoFmzZsycOZNDhw4FsGWiKosXL/Z6Ynnz5s1Zu3YtW7duZf369cyaNYvjx4/7qIXBR9M0zp49y5kzZ9B1nSZNmgAQGRlZZgJ7XRYs17BHH33Udf/BBx+kWbNmLFy4kH79+gV84cCFSgKeIBNMQYJz1Unpb0Pp6enMnj27Xq06CWbPPPMMXbp0YfDgwVx//fW8/PLLAPz4449cfvnl2O12/vrXv5KYmEhycjKvv/46YOQGeuSRR0hJSaFv375s3ryZK664gg4dOrh6ckJCQlyruYqKitA0LTBvsp7Izs6mT58+9O3bl8zMTE6cOAFAbm5uwHMvVUcwXcOcNm7cyLPPPkubNm24//77SUtLC3STLkj1J+wXHgmWpakAn332GbNmzWLIkCFlVp18/vnngW7eBe+3337jyy+/JDU1FavVSu/evenTpw8ZGRlYLBZiY2N5++23SUtLY+vWrZjNZjIzM13Pb926NVu3buX+++/n1ltv5ZdffqGwsJDExERmzJgBGKtaxo4dy759+3jppZdISEgI1Nut8yr6EFVVla+//rp2G+OFYLmGnTp1ildeeQVd1zl37hy6rrsyLEvwHhjSwxNkPvvsM86cOcOQIUOIi4sjLi6OoUOHkpmZyRdffBHo5lVLw4YNmTp1Km+88QZHjhwhMzOTnTt3Mnv2bNfkTBE4v/zyCxMmTCAsLIzo6GiuvPJKwMgw61wVtHz5cu644w7XkErppdHO2lVJSUkMGDCA6OhomjRpQmhoqGuuRqtWrdi2bRv79u3j448/lrkPNRAREUG7du0C3QyPBcs1bNq0aeTk5JCbm8stt9xCRkYGYGS/TklJCWzjLlDSwxNkGjZsyOzZs5k9e3aZfR9++CFTp04NQKtqpnQBzNtvv92tAOajjz4qyQfrqMWLF/PAAw9UeVzp5IPO+87HNpvN7diEhAQSExNZvXo11113nW8bLOqUYLmGPfHEE+Vub9asGcOGDavl1giQHp4LSkX/AOuq999/n02bNvHNN9+wcuVKnnnmGV599VWAejUnIVgNGjSIhQsXUlhYSG5uLosWLULXdbZt2+b6BjtixAjeffddVwBTekirKkePHqWgoAAwkuqtWbOGLl26+Px9iPqjvl3DKhIs76O+kR6eIJOcnFzudl3X691wQF0ugCmgX79+jB8/nuTkZOLj40lKSgKgV69errkKt99+O3v27CE5ORmLxcK0adO4++67PTr/zp07efDBB12Vy//617+6XkMEr2C5hgXL+wgmkngwyMTHx/PDDz/QsGFDt+26rjNw4MB6tax3+PDhvPLKK27j3Tabjdtuu425c+dit9sD1zgBGCuAoqKiyM/P59JLL2X8+PF07tyZyZMnB7ppop4KlmtYsLyPYCI9PEFm3Lhx5ObmljspbujQobXeHm/U5QKYwjB9+nR27NhBYWEht9xyi9QHEl4LlmtYsLyPYCI9PEIIIYQIejJpWQghhBBBTwIeIYQQQgQ9CXhEpWw2G02aNClTaXno0KF06dKFnj17MmjQIHbv3u3avnHjxkA0VQhxAXNm+wYjh1e3bt248cYb+eabb9ixY0eAWyfqAgl4RKWWLVtG586d+eKLL8osBZ87dy6pqanccsstPPTQQwFqoRBCwJo1axg0aBAAb731FsuWLWPu3LkS8AgXCXgEUHERyHnz5nHffffRunVr1q5dW+5zL730Uvbt21ebzRVCBLm0tDQSExNdj19++WWefPJJXnvtNbp3705ycrJb+oMlS5YwevRoZsyYwYEDBxg9ejTPPfccCxYs4KGHHiIlJYX9+/cH4q2IOkKWpYsKi0AWFhayfPly3n33XbKyspg3bx4DBw4s8/yFCxdKQjghRK2YNWsWBw8edKu5BrBixQqeeOIJxowZw5IlS1ixYgWNGzdm7969jBs3TkqSCOnhERUXgVy0aBHDhg0jPDyca6+9lm+++cYt2d+NN95ISkoKv/zyi6tHSAgh/Ck5OZkbb7yRTz75xJWn69ixY8TFxRERERHg1om6THp4RIXmzZvHmjVraNu2LQBnzpzhp59+YsSIEYAxh6dv374BbKEQIliZzWY0TXM9LiwsBOC7775j1apVLFy4kOeee47t27ezZMkSrrjiikA1VdQT0sMjyi0CmZ+fz+rVqzl8+DBpaWmkpaXx5ptvMm/evEA3VwhxAYiPj+fUqVOcOXOGoqIiFi1ahKZpHDlyhGHDhjF79myys7PJzc11zd8pT3R0NDk5ObXcelEXScAj3IpAjh49mqSkJHRdZ/jw4YSGhrqOmzBhAgsXLqSoqCiArRVCXAgsFgv/+Mc/6N+/PyNGjKBr167Y7XamTJlCUlISvXr14t577yU6Opp9+/bRtWvXcs8zefJkXnrpJXr16iWTli9wUlpCAGWLQL733nuunBZCCFFXrVmzhk8++YR33nkn0E0RdZwEPAKAG264QYpACiGECFoS8AghhBAi6MkcHiGEEEIEPQl4hBBCCBH0JOARQgghRNCTgEcIIYQQQU8CHiGEEEIEPQl4hBBCCBH0/h+/2JIRKw+BDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x864 with 5 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracks = ['MD', 'GR', 'RHOB', ['DT', 'DTS'], 'MD']\n",
    "\n",
    "well.plot(tracks=tracks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Location and CRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location({'position': None, 'crs': CRS({}), 'location': 'Lat = 45* 12\\' 34.237\" N', 'country': 'CA', 'province': 'Nova Scotia', 'latitude': '', 'longitude': '', 'datum': '', 'section': '45.20 Deg N', 'range': 'PD 176', 'township': '63.75 Deg W', 'ekb': 94.8, 'egl': 90.3, 'gl': 90.3, 'tdd': 1935.0, 'tdl': 1935.0, 'td': None, 'deviation': None})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export curves to data matrix\n",
    "\n",
    "Make a NumPy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[Curve(mnemonic=CALI, units=in, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=HCAL, units=in, start=1.0668, stop=1939.1376, step=0.1524, count=[2138]),\n",
       "        Curve(mnemonic=PEF, units=, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=DT, units=us/ft, start=1.0668, stop=1939.1376, step=0.1524, count=[10849]),\n",
       "        Curve(mnemonic=DTS, units=us/ft, start=1.0668, stop=1939.1376, step=0.1524, count=[10849]),\n",
       "        Curve(mnemonic=DPHI_SAN, units=m3/m3, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=DPHI_LIM, units=m3/m3, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=DPHI_DOL, units=m3/m3, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=NPHI_SAN, units=m3/m3, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=NPHI_LIM, units=m3/m3, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=NPHI_DOL, units=m3/m3, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RLA5, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RLA3, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RLA4, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RLA1, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RLA2, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RXOZ, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RXO_HRLT, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[2138]),\n",
       "        Curve(mnemonic=RT_HRLT, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RM_HRLT, units=ohm.m, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=DRHO, units=g/cm3, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=RHOB, units=g/cm3, start=1.0668, stop=1939.1376, step=0.1524, count=[12706]),\n",
       "        Curve(mnemonic=GR, units=gAPI, start=1.0668, stop=1939.1376, step=0.1524, count=[12717]),\n",
       "        Curve(mnemonic=SP, units=mV, start=1.0668, stop=1939.1376, step=0.1524, count=[12717])]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.data_as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export curves to pandas\n",
    "\n",
    "Pandas is an optional dependency. You'll need it to make this work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = w.df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALI</th>\n",
       "      <th>HCAL</th>\n",
       "      <th>PEF</th>\n",
       "      <th>DT</th>\n",
       "      <th>DTS</th>\n",
       "      <th>DPHI_SAN</th>\n",
       "      <th>DPHI_LIM</th>\n",
       "      <th>DPHI_DOL</th>\n",
       "      <th>NPHI_SAN</th>\n",
       "      <th>NPHI_LIM</th>\n",
       "      <th>...</th>\n",
       "      <th>RLA1</th>\n",
       "      <th>RLA2</th>\n",
       "      <th>RXOZ</th>\n",
       "      <th>RXO_HRLT</th>\n",
       "      <th>RT_HRLT</th>\n",
       "      <th>RM_HRLT</th>\n",
       "      <th>DRHO</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>GR</th>\n",
       "      <th>SP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0668</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2192</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3716</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
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       "    <tr>\n",
       "      <th>1.5240</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.6764</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                CALI          HCAL          PEF  DT  DTS      DPHI_SAN  \\\n",
       "DEPT                                                                     \n",
       "1.0668  2.4438154697  4.3912849426  3.586400032 NaN  NaN  0.1574800014   \n",
       "1.2192  2.4438154697  4.3912849426  3.586400032 NaN  NaN  0.1574800014   \n",
       "1.3716  2.4438154697  4.3912849426  3.586400032 NaN  NaN  0.1574800014   \n",
       "1.5240  2.4438154697  4.3912849426  3.586400032 NaN  NaN  0.1574800014   \n",
       "1.6764  2.4438154697  4.3912849426  3.586400032 NaN  NaN  0.1574800014   \n",
       "\n",
       "            DPHI_LIM      DPHI_DOL      NPHI_SAN      NPHI_LIM  ...  \\\n",
       "DEPT                                                            ...   \n",
       "1.0668  0.1984400004  0.2590999901  0.4650999904  0.3364700079  ...   \n",
       "1.2192  0.1984400004  0.2590999901  0.4650999904  0.3364700079  ...   \n",
       "1.3716  0.1984400004  0.2590999901  0.4650999904  0.3364700079  ...   \n",
       "1.5240  0.1984400004  0.2590999901  0.4650999904  0.3364700079  ...   \n",
       "1.6764  0.1984400004  0.2590999901  0.4650999904  0.3364700079  ...   \n",
       "\n",
       "                RLA1          RLA2          RXOZ      RXO_HRLT       RT_HRLT  \\\n",
       "DEPT                                                                           \n",
       "1.0668  0.0320999995  0.0279399995  0.0576100014  0.0255800001  0.0255800001   \n",
       "1.2192  0.0320999995  0.0279399995  0.0576100014  0.0255800001  0.0255800001   \n",
       "1.3716  0.0320999995  0.0279399995  0.0576100014  0.0255800001  0.0255800001   \n",
       "1.5240  0.0320999995  0.0279399995  0.0576100014  0.0255800001  0.0255800001   \n",
       "1.6764  0.0320999995  0.0279399995  0.0576100014  0.0255800001  0.0255800001   \n",
       "\n",
       "             RM_HRLT          DRHO          RHOB           GR       SP  \n",
       "DEPT                                                                    \n",
       "1.0668  0.0550099984  0.1942329407  2.3901498318  46.69865036  120.125  \n",
       "1.2192  0.0550099984  0.1942329407  2.3901498318  46.69865036  120.125  \n",
       "1.3716  0.0550099984  0.1942329407  2.3901498318  46.69865036  120.125  \n",
       "1.5240  0.0550099984  0.1942329407  2.3901498318  46.69865036  120.125  \n",
       "1.6764  0.0550099984  0.1942329407  2.3901498318  46.69865036  120.125  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPT'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.GR.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This also gives us another path to getting a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.44381547,   4.39128494,   3.58640003, ...,   2.39014983,\n",
       "         46.69865036, 120.125     ],\n",
       "       [  2.44381547,   4.39128494,   3.58640003, ...,   2.39014983,\n",
       "         46.69865036, 120.125     ],\n",
       "       [  2.44381547,   4.39128494,   3.58640003, ...,   2.39014983,\n",
       "         46.69865036, 120.125     ],\n",
       "       ...,\n",
       "       [  2.42026806,          nan,   2.23697996, ...,          nan,\n",
       "         92.24622345,  73.        ],\n",
       "       [  2.42026806,          nan,   2.23697996, ...,          nan,\n",
       "         92.24622345,  73.9375    ],\n",
       "       [  2.42026806,          nan,   2.23697996, ...,          nan,\n",
       "         92.24622345,  74.25      ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.df().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll have to get depth separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.0668000e+00, 1.2192000e+00, 1.3716000e+00, ..., 1.9388328e+03,\n",
       "       1.9389852e+03, 1.9391376e+03])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.df().index.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the UWI of the well as well, e.g. if you want to combine multiple wells (maybe using `welly.Project.df()`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = w.df(uwi=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>CALI</th>\n",
       "      <th>HCAL</th>\n",
       "      <th>PEF</th>\n",
       "      <th>DT</th>\n",
       "      <th>DTS</th>\n",
       "      <th>DPHI_SAN</th>\n",
       "      <th>DPHI_LIM</th>\n",
       "      <th>DPHI_DOL</th>\n",
       "      <th>NPHI_SAN</th>\n",
       "      <th>NPHI_LIM</th>\n",
       "      <th>...</th>\n",
       "      <th>RLA1</th>\n",
       "      <th>RLA2</th>\n",
       "      <th>RXOZ</th>\n",
       "      <th>RXO_HRLT</th>\n",
       "      <th>RT_HRLT</th>\n",
       "      <th>RM_HRLT</th>\n",
       "      <th>DRHO</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>GR</th>\n",
       "      <th>SP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UWI</th>\n",
       "      <th>DEPT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Long = 63* 45'24.460  W</th>\n",
       "      <th>1.0668</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2192</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3716</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5240</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.6764</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>4.3912849426</td>\n",
       "      <td>3.586400032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1574800014</td>\n",
       "      <td>0.1984400004</td>\n",
       "      <td>0.2590999901</td>\n",
       "      <td>0.4650999904</td>\n",
       "      <td>0.3364700079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0320999995</td>\n",
       "      <td>0.0279399995</td>\n",
       "      <td>0.0576100014</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0255800001</td>\n",
       "      <td>0.0550099984</td>\n",
       "      <td>0.1942329407</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>120.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        CALI          HCAL          PEF  DT  \\\n",
       "UWI                     DEPT                                                  \n",
       "Long = 63* 45'24.460  W 1.0668  2.4438154697  4.3912849426  3.586400032 NaN   \n",
       "                        1.2192  2.4438154697  4.3912849426  3.586400032 NaN   \n",
       "                        1.3716  2.4438154697  4.3912849426  3.586400032 NaN   \n",
       "                        1.5240  2.4438154697  4.3912849426  3.586400032 NaN   \n",
       "                        1.6764  2.4438154697  4.3912849426  3.586400032 NaN   \n",
       "\n",
       "                                DTS      DPHI_SAN      DPHI_LIM      DPHI_DOL  \\\n",
       "UWI                     DEPT                                                    \n",
       "Long = 63* 45'24.460  W 1.0668  NaN  0.1574800014  0.1984400004  0.2590999901   \n",
       "                        1.2192  NaN  0.1574800014  0.1984400004  0.2590999901   \n",
       "                        1.3716  NaN  0.1574800014  0.1984400004  0.2590999901   \n",
       "                        1.5240  NaN  0.1574800014  0.1984400004  0.2590999901   \n",
       "                        1.6764  NaN  0.1574800014  0.1984400004  0.2590999901   \n",
       "\n",
       "                                    NPHI_SAN      NPHI_LIM  ...          RLA1  \\\n",
       "UWI                     DEPT                                ...                 \n",
       "Long = 63* 45'24.460  W 1.0668  0.4650999904  0.3364700079  ...  0.0320999995   \n",
       "                        1.2192  0.4650999904  0.3364700079  ...  0.0320999995   \n",
       "                        1.3716  0.4650999904  0.3364700079  ...  0.0320999995   \n",
       "                        1.5240  0.4650999904  0.3364700079  ...  0.0320999995   \n",
       "                        1.6764  0.4650999904  0.3364700079  ...  0.0320999995   \n",
       "\n",
       "                                        RLA2          RXOZ      RXO_HRLT  \\\n",
       "UWI                     DEPT                                               \n",
       "Long = 63* 45'24.460  W 1.0668  0.0279399995  0.0576100014  0.0255800001   \n",
       "                        1.2192  0.0279399995  0.0576100014  0.0255800001   \n",
       "                        1.3716  0.0279399995  0.0576100014  0.0255800001   \n",
       "                        1.5240  0.0279399995  0.0576100014  0.0255800001   \n",
       "                        1.6764  0.0279399995  0.0576100014  0.0255800001   \n",
       "\n",
       "                                     RT_HRLT       RM_HRLT          DRHO  \\\n",
       "UWI                     DEPT                                               \n",
       "Long = 63* 45'24.460  W 1.0668  0.0255800001  0.0550099984  0.1942329407   \n",
       "                        1.2192  0.0255800001  0.0550099984  0.1942329407   \n",
       "                        1.3716  0.0255800001  0.0550099984  0.1942329407   \n",
       "                        1.5240  0.0255800001  0.0550099984  0.1942329407   \n",
       "                        1.6764  0.0255800001  0.0550099984  0.1942329407   \n",
       "\n",
       "                                        RHOB           GR       SP  \n",
       "UWI                     DEPT                                        \n",
       "Long = 63* 45'24.460  W 1.0668  2.3901498318  46.69865036  120.125  \n",
       "                        1.2192  2.3901498318  46.69865036  120.125  \n",
       "                        1.3716  2.3901498318  46.69865036  120.125  \n",
       "                        1.5240  2.3901498318  46.69865036  120.125  \n",
       "                        1.6764  2.3901498318  46.69865036  120.125  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas with an alias dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Gamma': ['GR', 'GAM', 'GRC', 'SGR', 'NGT'],\n",
       " 'Density': ['RHOZ', 'RHOB', 'DEN', 'RHOZ'],\n",
       " 'Sonic': ['DT', 'AC', 'DTP', 'DT4P'],\n",
       " 'Caliper': ['CAL', 'CALI', 'CALS', 'C1'],\n",
       " 'Porosity SS': ['NPSS', 'DPSS']}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALI</th>\n",
       "      <th>Gamma</th>\n",
       "      <th>Density</th>\n",
       "      <th>Sonic</th>\n",
       "      <th>RLA1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0668</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0320999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2192</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0320999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3716</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0320999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5240</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0320999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.6764</th>\n",
       "      <td>2.4438154697</td>\n",
       "      <td>46.69865036</td>\n",
       "      <td>2.3901498318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0320999995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CALI        Gamma       Density  Sonic          RLA1\n",
       "DEPT                                                                \n",
       "1.0668  2.4438154697  46.69865036  2.3901498318    NaN  0.0320999995\n",
       "1.2192  2.4438154697  46.69865036  2.3901498318    NaN  0.0320999995\n",
       "1.3716  2.4438154697  46.69865036  2.3901498318    NaN  0.0320999995\n",
       "1.5240  2.4438154697  46.69865036  2.3901498318    NaN  0.0320999995\n",
       "1.6764  2.4438154697  46.69865036  2.3901498318    NaN  0.0320999995"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys = ['CALI', 'Gamma', 'Density', 'Sonic', 'RLA1']\n",
    "w.df(keys=keys, alias=alias, rename_aliased=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make an 'empty' well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_mnemonic</th>\n",
       "      <th>mnemonic</th>\n",
       "      <th>unit</th>\n",
       "      <th>value</th>\n",
       "      <th>descr</th>\n",
       "      <th>section</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [original_mnemonic, mnemonic, unit, value, descr, section]\n",
       "Index: []"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = welly.Well()\n",
    "\n",
    "w.header  # is empty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can set the **UWI** and **name** of a well directly on the well object, but these are the only attributes of the well we can set in this way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'foo'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.uwi = 'foo'\n",
    "w.uwi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_mnemonic</th>\n",
       "      <th>mnemonic</th>\n",
       "      <th>unit</th>\n",
       "      <th>value</th>\n",
       "      <th>descr</th>\n",
       "      <th>section</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UWI</td>\n",
       "      <td>UWI</td>\n",
       "      <td>None</td>\n",
       "      <td>foo</td>\n",
       "      <td>None</td>\n",
       "      <td>header</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  original_mnemonic mnemonic  unit value descr section\n",
       "0               UWI      UWI  None   foo  None  header"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "&copy; 2022 Agile Scientific, CC BY"
   ]
  }
 ],
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